From b74d8f996e5ced623b47f5d035e5f2ecf83c475d Mon Sep 17 00:00:00 2001 From: liu-yihong Date: Wed, 12 Jul 2023 00:52:22 -0500 Subject: [PATCH] Update Job page and theme --- assets/javascripts/bundle.220ee61c.min.js | 29 ++++++++++++ assets/javascripts/bundle.220ee61c.min.js.map | 8 ++++ .../workers/search.74e28a9f.min.js | 42 ++++++++++++++++++ .../workers/search.74e28a9f.min.js.map | 8 ++++ conferences/calendar/index.html | 4 +- conferences/index.html | 2 +- index.html | 4 +- intro/index.html | 2 +- jobs/index.html | 2 +- journals/index.html | 2 +- references/index.html | 2 +- research/analytical/index.html | 2 +- research/empirical/index.html | 2 +- research/technical/index.html | 2 +- search/search_index.json | 2 +- sitemap.xml | 20 ++++----- sitemap.xml.gz | Bin 292 -> 292 bytes 17 files changed, 110 insertions(+), 23 deletions(-) create mode 100644 assets/javascripts/bundle.220ee61c.min.js create mode 100644 assets/javascripts/bundle.220ee61c.min.js.map create mode 100644 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\ No newline at end of file diff --git a/conferences/index.html b/conferences/index.html index 0405927..12b1728 100644 --- a/conferences/index.html +++ b/conferences/index.html @@ -1 +1 @@ - Conferences - MIS Reading List

MIS Conferences⚓︎

The International Conference on Information Systems (ICIS)

The International Conference on Information Systems (ICIS) is the most prestigious gathering of information systems academics and research-oriented practitioners in the world. Every year its 270 or so papers and panel presentations are selected from more than 800 submissions. The conference activities are primarily delivered by and for academics, though many of the papers and panels have a strong professional orientation.

ICIS was founded in 1980 at UCLA and the first conference was held at the University of Pennsylvania as the " Conference on Information Systems". By 1986, particularly as the result of Canadian and European attendance and participation, " International" was appended to the name, thereby creating the International Conference on Information Systems. ICIS became truly international in 1990 when the conference was first held outside North America in Copenhagen, Denmark.

---- ICIS Home Page / ICIS Proceedings / 2023

Conference on Information Systems and Technology (CIST): 2022, 2023

Workshop on Information Systems and Economics (WISE)

Workshop on Information Technologies and Systems (WITS)

WITS, the Workshop on Information Technologies and Systems is an academic conference for information systems that is held annually in December in conjunction with ICIS (the International Conference on Information Systems).

The WITS community is focused on addressing complex business problems or societal issues using current and emerging information technologies.  We also encourage research that can change the way information technology functions (e.g., by designing, modifying, or constructing systems) so that they can better solve real-world problems. All problem-solving paradigms – including empirical, analytical, behavioral, experimental, and computational – are invited. Integrative approaches, whether methodological or functional, are welcome.

WITS research is often prescriptive (toward providing a solution to a problem), rather than descriptive (explaining a phenomenon), unless the explanation clearly helps in developing a solution. We particularly invite work that is early, but has the potential to make a significant impact – innovation and novelty are at least as important as completeness and rigor.

---- WITS Home Page

Theory in Economics of Information Systems (TEIS)

The TEIS workshop is designed to provide a community for researchers who use analytical modeling techniques in the area of economics of information systems. Although a number of workshops and conferences accept research based on analytical models, these tend to be diffused with inadequate time for presentation, discussion and Q&A.

TEIS workshop complements such venues by providing a focused and intense environment for interaction among researchers to assist in the development of the field and help advance shared understanding about various aspects of modeling research. TEIS workshops have a single track with one hour per paper so everyone can participate substantively in the discussion.

---- TEIS Home Page

Statistical Challenges in Electronic Commerce Research (SCECR)

Started in 2005 by Ravi Bapna (currently at University of Minnesota), Wolfgang Jank (currently at University of South Florida), and Galit Schmueli (National Tsing Hua University), the Workshop on Statistical Conference in E-Commerce Research (SCECR) is a leading workshop attracting many top researchers throughout the world in the areas of information systems, quantitative marketing, economics,  statistics, machine learning, and computer science.

The workshop covers diverse domains such as e-commerce, social media, digital content, finance, and telecommunications.  Methods include econometric, machine learning, statistical inference, and unstructured and Big Data techniques.  The theme this year will be related to Big Data and economic impact.

---- SCECR Home Page

Conference on Health IT and Analytics (CHITA)

The Conference on Health IT and Analytics (previously known as the Workshop on Health IT & Economics) is an annual health IT and analytics research summit, including a doctoral consortium that each year gathers prominent scholars from more than 40 research institutes, and leading policy and practitioner attendees in a vibrant setting to discuss opportunities and challenges in the design, implementation and management of health information technology and analytics.

Its goal is to deepen our understanding of strategy, policy and systems fostering health IT and analytics effective use and to stimulate new ideas with both policy and business implications. This forum provides a productive venue to facilitate collaboration among academia, government, and industry. Now in its eleventh year, each year CHITA draws over 120 participants.

---- 2022 / 2023

Hawaii International Conference on System Sciences (HICSS)

Since 1968, the Hawaii International Conference on System Sciences (HICSS) has been known worldwide as the longest-standing working scientific conferences in Information Technology Management.

HICSS provides a highly interactive working environment for top scholars from academia and the industry from over 60 countries to exchange ideas in various areas of information, computer, and system sciences. According to Microsoft Academic, HICSS ranks the 36th in terms of citations among 4,444 conferences in all fields worldwide.

---- HICSS Home Page

China Summer Workshop on Information Management (CSWIM)

As China has become a major player in the world’s economy and various technological fields, information systems and management research opportunities are abundant for scholars around the globe.

The purpose of China Summer Workshop on Information Management (CSWIM) is to create a new bridge for promoting exchanges between scholars in China and overseas in the area of information systems and management. In particular, CSWIM focuses on creating a unique experience for MIS researchers around the world who would like to communicate and collaborate with China-based scholars.

---- CSWIM Home Page

China Workshop on Economics of Information Systems Theory (CWEIST)

The field of information systems has a long tradition of using analytical modeling (e.g. game-theoretical and mathematical models) to understand information systems phenomena and generate useful recommendations. With many emerging phenomena in IS, the need for this type of applied theory research is ever greater. However, the forums for this style of inquiry are rather limited, especially for analytical modeling scholars in China and the surrounding regions. The purpose of the China Workshop on Economics of Information Systems Theory (CWEIST) is to bring together a community of scholars in China and around the world with a shared interest in using analytical modeling to study issues in IS and related fields. We hope this summer workshop to become a unique forum for this community to exchange ideas, hone our skills, and form new collaborations across geographical boundaries.

---- CWEIST Home Page / 2023

Production and Operations Management Society (POMS) Conference

Production and Operations Management Society (POMS) is an international professional organization representing the interests of POM professionals from around the world.

The purposes of the Society are:

  • to extend and integrate knowledge that contributes to the improved understanding and practice of production and operations management (POM);
  • to disseminate information on POM to managers, scientists, educators, students, public and private organizations, national and local governments, and the general public; and
  • to promote the improvement of POM and its teaching in public and private manufacturing and service organizations throughout the world

---- POMS Home Page / POMS Conference Page / 2023

INFORMS Annual Meeting

The INFORMS Annual Meeting brings together over 6,000 people to the world's largest O.R. and analytics conference. Held each fall, the INFORMS Annual Meeting features more than 800 sessions and presentations, opportunities to meet with leading companies, universities and other exhibitors, an onsite career fair connecting top talent with over 100 organizations at the forefront of O.R. and analytics application, and other networking and educational events.

---- INFORMS Conference Home Page / 2023 INFORMS Annual Meeting

The Americas Conference on Information Systems (AMCIS)

The annual Americas Conference on Information Systems (AMCIS) is viewed as one of the leading conferences for presenting the broadest variety of research done by and for IS/IT academicians. Every year its papers and panel presentations are selected from over 700 submissions, and the AMCIS proceedings are in the permanent collections of libraries throughout the world.

---- AMCIS Home Page / AMCIS Proceedings

Pacific Asia Conference on Information Systems (PACIS)

The annual Pacific Asia Conference on Information Systems (PACIS) is viewed as one of the leading conferences on information systems and the only AIS conference dedicated to the Pacific Asia Region. PACIS is endorsed by the AIS Council and governed by the AIS Region 3 Board.

---- PACIS Home Page / PACIS Proceedings

European Conference on Information Systems (ECIS)

The annual European Conference on Information Systems (ECIS) is viewed as one of the leading conferences on information systems and the only AIS conference dedicated to the European Region. ECIS is the newest regional conference endorsed by the AIS Council and governed by the AIS Region 2 Board.

---- ECIS Home Page / ECIS Proceedings

International Conference on Design Science Research in Information Systems and Technology (DESRIST)

Design science research (DSR) in information systems (IS) has received significant attention in the information systems research community. In an immersed society, where there are numerous wicked problems on all levels of analysis, DSR is an ideal approach to understand  complex challenges and support the design of useful solutions, making provision for rigour and relevance. Based on multi-stakeholder problem analysis and informed by existing descriptive and design knowledge, well-designed innovative methods, solution patterns, reference models and exemplary IS solutions promise to be effective means of addressing many of today’s challenges – and will contribute to the further development of DSR’s methodological foundations. The better we get at integrating humans, organisations and machines, the better we will be able to use all means possible to achieve the Sustainable Development Goals (SDGs). The United Nations, with its economic and social development agenda, as it pertains to sustainability, ultimately impacts all countries, organisations, teams and individuals through the SDGs.

---- 2023 / 2022 / 2021 / Springer Conference Proceedings List

IADIS Information Systems Conference

The IADIS Information Systems Conference aims to provide a forum for the discussion of IS taking a socio-technological perspective. It aims to address the issues related to design, development and use of IS in organisations from a socio-technological perspective, as well as to discuss IS professional practice, research and teaching.

---- 2023 / IADIS

International Conference on Information Systems Security and Privacy (ICISSP)

The International Conference on Information Systems Security and Privacy provides a meeting point for researchers and practitioners, addressing the trust, security and privacy challenges of information systems from both technological and social perspectives.

The conference welcomes papers of either practical or theoretical nature, and is interested in research or applications addressing all aspects of trust, security and privacy, and encompassing issue of concern for organizations, individuals and society at large.

---- ICISSP Home Page

INFORMS Conference Calendar

Conference Index - Information Systems Conferences

© License⚓︎

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Last Update On 2023-04-05.

Copyright © 2019 - 2023 Yihong Liu
\ No newline at end of file + Conferences - MIS Reading List

MIS Conferences⚓︎

The International Conference on Information Systems (ICIS)

The International Conference on Information Systems (ICIS) is the most prestigious gathering of information systems academics and research-oriented practitioners in the world. Every year its 270 or so papers and panel presentations are selected from more than 800 submissions. The conference activities are primarily delivered by and for academics, though many of the papers and panels have a strong professional orientation.

ICIS was founded in 1980 at UCLA and the first conference was held at the University of Pennsylvania as the " Conference on Information Systems". By 1986, particularly as the result of Canadian and European attendance and participation, " International" was appended to the name, thereby creating the International Conference on Information Systems. ICIS became truly international in 1990 when the conference was first held outside North America in Copenhagen, Denmark.

---- ICIS Home Page / ICIS Proceedings / 2023

Conference on Information Systems and Technology (CIST): 2022, 2023

Workshop on Information Systems and Economics (WISE)

Workshop on Information Technologies and Systems (WITS)

WITS, the Workshop on Information Technologies and Systems is an academic conference for information systems that is held annually in December in conjunction with ICIS (the International Conference on Information Systems).

The WITS community is focused on addressing complex business problems or societal issues using current and emerging information technologies.  We also encourage research that can change the way information technology functions (e.g., by designing, modifying, or constructing systems) so that they can better solve real-world problems. All problem-solving paradigms – including empirical, analytical, behavioral, experimental, and computational – are invited. Integrative approaches, whether methodological or functional, are welcome.

WITS research is often prescriptive (toward providing a solution to a problem), rather than descriptive (explaining a phenomenon), unless the explanation clearly helps in developing a solution. We particularly invite work that is early, but has the potential to make a significant impact – innovation and novelty are at least as important as completeness and rigor.

---- WITS Home Page

Theory in Economics of Information Systems (TEIS)

The TEIS workshop is designed to provide a community for researchers who use analytical modeling techniques in the area of economics of information systems. Although a number of workshops and conferences accept research based on analytical models, these tend to be diffused with inadequate time for presentation, discussion and Q&A.

TEIS workshop complements such venues by providing a focused and intense environment for interaction among researchers to assist in the development of the field and help advance shared understanding about various aspects of modeling research. TEIS workshops have a single track with one hour per paper so everyone can participate substantively in the discussion.

---- TEIS Home Page

Statistical Challenges in Electronic Commerce Research (SCECR)

Started in 2005 by Ravi Bapna (currently at University of Minnesota), Wolfgang Jank (currently at University of South Florida), and Galit Schmueli (National Tsing Hua University), the Workshop on Statistical Conference in E-Commerce Research (SCECR) is a leading workshop attracting many top researchers throughout the world in the areas of information systems, quantitative marketing, economics,  statistics, machine learning, and computer science.

The workshop covers diverse domains such as e-commerce, social media, digital content, finance, and telecommunications.  Methods include econometric, machine learning, statistical inference, and unstructured and Big Data techniques.  The theme this year will be related to Big Data and economic impact.

---- SCECR Home Page

Conference on Health IT and Analytics (CHITA)

The Conference on Health IT and Analytics (previously known as the Workshop on Health IT & Economics) is an annual health IT and analytics research summit, including a doctoral consortium that each year gathers prominent scholars from more than 40 research institutes, and leading policy and practitioner attendees in a vibrant setting to discuss opportunities and challenges in the design, implementation and management of health information technology and analytics.

Its goal is to deepen our understanding of strategy, policy and systems fostering health IT and analytics effective use and to stimulate new ideas with both policy and business implications. This forum provides a productive venue to facilitate collaboration among academia, government, and industry. Now in its eleventh year, each year CHITA draws over 120 participants.

---- 2022 / 2023

Hawaii International Conference on System Sciences (HICSS)

Since 1968, the Hawaii International Conference on System Sciences (HICSS) has been known worldwide as the longest-standing working scientific conferences in Information Technology Management.

HICSS provides a highly interactive working environment for top scholars from academia and the industry from over 60 countries to exchange ideas in various areas of information, computer, and system sciences. According to Microsoft Academic, HICSS ranks the 36th in terms of citations among 4,444 conferences in all fields worldwide.

---- HICSS Home Page

China Summer Workshop on Information Management (CSWIM)

As China has become a major player in the world’s economy and various technological fields, information systems and management research opportunities are abundant for scholars around the globe.

The purpose of China Summer Workshop on Information Management (CSWIM) is to create a new bridge for promoting exchanges between scholars in China and overseas in the area of information systems and management. In particular, CSWIM focuses on creating a unique experience for MIS researchers around the world who would like to communicate and collaborate with China-based scholars.

---- CSWIM Home Page

China Workshop on Economics of Information Systems Theory (CWEIST)

The field of information systems has a long tradition of using analytical modeling (e.g. game-theoretical and mathematical models) to understand information systems phenomena and generate useful recommendations. With many emerging phenomena in IS, the need for this type of applied theory research is ever greater. However, the forums for this style of inquiry are rather limited, especially for analytical modeling scholars in China and the surrounding regions. The purpose of the China Workshop on Economics of Information Systems Theory (CWEIST) is to bring together a community of scholars in China and around the world with a shared interest in using analytical modeling to study issues in IS and related fields. We hope this summer workshop to become a unique forum for this community to exchange ideas, hone our skills, and form new collaborations across geographical boundaries.

---- CWEIST Home Page / 2023

Production and Operations Management Society (POMS) Conference

Production and Operations Management Society (POMS) is an international professional organization representing the interests of POM professionals from around the world.

The purposes of the Society are:

  • to extend and integrate knowledge that contributes to the improved understanding and practice of production and operations management (POM);
  • to disseminate information on POM to managers, scientists, educators, students, public and private organizations, national and local governments, and the general public; and
  • to promote the improvement of POM and its teaching in public and private manufacturing and service organizations throughout the world

---- POMS Home Page / POMS Conference Page / 2023

INFORMS Annual Meeting

The INFORMS Annual Meeting brings together over 6,000 people to the world's largest O.R. and analytics conference. Held each fall, the INFORMS Annual Meeting features more than 800 sessions and presentations, opportunities to meet with leading companies, universities and other exhibitors, an onsite career fair connecting top talent with over 100 organizations at the forefront of O.R. and analytics application, and other networking and educational events.

---- INFORMS Conference Home Page / 2023 INFORMS Annual Meeting

The Americas Conference on Information Systems (AMCIS)

The annual Americas Conference on Information Systems (AMCIS) is viewed as one of the leading conferences for presenting the broadest variety of research done by and for IS/IT academicians. Every year its papers and panel presentations are selected from over 700 submissions, and the AMCIS proceedings are in the permanent collections of libraries throughout the world.

---- AMCIS Home Page / AMCIS Proceedings

Pacific Asia Conference on Information Systems (PACIS)

The annual Pacific Asia Conference on Information Systems (PACIS) is viewed as one of the leading conferences on information systems and the only AIS conference dedicated to the Pacific Asia Region. PACIS is endorsed by the AIS Council and governed by the AIS Region 3 Board.

---- PACIS Home Page / PACIS Proceedings

European Conference on Information Systems (ECIS)

The annual European Conference on Information Systems (ECIS) is viewed as one of the leading conferences on information systems and the only AIS conference dedicated to the European Region. ECIS is the newest regional conference endorsed by the AIS Council and governed by the AIS Region 2 Board.

---- ECIS Home Page / ECIS Proceedings

International Conference on Design Science Research in Information Systems and Technology (DESRIST)

Design science research (DSR) in information systems (IS) has received significant attention in the information systems research community. In an immersed society, where there are numerous wicked problems on all levels of analysis, DSR is an ideal approach to understand  complex challenges and support the design of useful solutions, making provision for rigour and relevance. Based on multi-stakeholder problem analysis and informed by existing descriptive and design knowledge, well-designed innovative methods, solution patterns, reference models and exemplary IS solutions promise to be effective means of addressing many of today’s challenges – and will contribute to the further development of DSR’s methodological foundations. The better we get at integrating humans, organisations and machines, the better we will be able to use all means possible to achieve the Sustainable Development Goals (SDGs). The United Nations, with its economic and social development agenda, as it pertains to sustainability, ultimately impacts all countries, organisations, teams and individuals through the SDGs.

---- 2023 / 2022 / 2021 / Springer Conference Proceedings List

IADIS Information Systems Conference

The IADIS Information Systems Conference aims to provide a forum for the discussion of IS taking a socio-technological perspective. It aims to address the issues related to design, development and use of IS in organisations from a socio-technological perspective, as well as to discuss IS professional practice, research and teaching.

---- 2023 / IADIS

International Conference on Information Systems Security and Privacy (ICISSP)

The International Conference on Information Systems Security and Privacy provides a meeting point for researchers and practitioners, addressing the trust, security and privacy challenges of information systems from both technological and social perspectives.

The conference welcomes papers of either practical or theoretical nature, and is interested in research or applications addressing all aspects of trust, security and privacy, and encompassing issue of concern for organizations, individuals and society at large.

---- ICISSP Home Page

INFORMS Conference Calendar

Conference Index - Information Systems Conferences

© License⚓︎

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Last Update On 2023-04-05.

Copyright © 2019 - 2023 Yihong Liu
\ No newline at end of file diff --git a/index.html b/index.html index 043fc52..79f23a5 100644 --- a/index.html +++ b/index.html @@ -1,4 +1,4 @@ - MIS Reading List

What Should I Read On Management Information Systems?

This is a reading list for Ph.D. students and researchers majoring in Management Information Systems.

Start Reading!

Home

Copyright © 2019 - 2023 Yihong Liu
\ No newline at end of file + /> -->

What Should I Read On Management Information Systems?

This is a reading list for Ph.D. students and researchers majoring in Management Information Systems.

Start Reading!

Home

Copyright © 2019 - 2023 Yihong Liu
\ No newline at end of file diff --git a/intro/index.html b/intro/index.html index bc52545..1246941 100644 --- a/intro/index.html +++ b/intro/index.html @@ -1 +1 @@ - Introduction - MIS Reading List

Introduction⚓︎

What to read as a Ph.D. student or researcher majoring in Management Information Systems (MIS)?

This reading list covers MIS-relevant journals, conferences, books, papers, and resources.

A considerable number of papers in the list comes from the MIS7420 Seminar in Management Information Systems.

Ordered alphabetically, Prof. Amit Mehra, Atanu Lahiri, Jianqing Chen, Srinivasan Raghunathan, Sumit Sarkar, Syam Menon, Vijay Mookerjee, Zhiqiang Zheng are in charge of this course and contribute a lot to this list.

Thanks to them for creating a diverse topic portfolio in this MIS reading list!

What is MIS?⚓︎

  • Monideepa Tarafdar, Guohou Shan, Jason Bennett Thatcher, Alok Gupta (2022) Intellectual Diversity in IS Research: Discipline-Based Conceptualization and an Illustration from Information Systems Research. Information Systems Research 33(4):1490-1510. source
IS Methods and Theories

Information systems researchers use a bevvy of research methods and theoretical lenses to explore phenomena of interest. The following links will take you to sites that have been developed by members of the IS community who are experts in particular areas.

Theories in IS Research

Design Research

Qualitative Research

Quantitative Research

Spatial Design Support Systems

Research Task Repository

Decision Support Systems

---- AIS - IS Research, Methods, and Theories

✍🏼 About The Author⚓︎

Yihong Liu is a Ph.D. candidate in the Management Science, Information Systems Concentration at the Naveen Jindal School of Management, UT Dallas.

ℹ Disclaimer⚓︎

Click to expand and read the disclaimer.

Last updated: March 08, 2022

Interpretation and Definitions⚓︎

Interpretation⚓︎

The words of which the initial letter is capitalized have meanings defined under the following conditions.

The following definitions shall have the same meaning regardless of whether they appear in singular or in plural.

Definitions⚓︎

For the purposes of this Disclaimer:

  • We (referred to as either "We", "Us" or "Our" in this Disclaimer) refers to the authors of "MIS Reading List".

  • Service refers to the Website.

  • You means the individual accessing the Service, or the company, or other legal entity on behalf of which such individual is accessing or using the Service, as applicable.

  • Website refers to MIS Reading List, accessible from https://liu-yihong.github.io/MISReadingList/

Disclaimer⚓︎

The information contained on the Service is for general information purposes only.

We assume no responsibility for errors or omissions in the contents of the Service.

In no event shall we be liable for any special, direct, indirect, consequential, or incidental damages or any damages whatsoever, whether in an action of contract, negligence or other tort, arising out of or in connection with the use of the Service or the contents of the Service. We reserve the right to make additions, deletions, or modifications to the contents on the Service at any time without prior notice.

We do not warrant that the Service is free of viruses or other harmful components.

The Service may contain links to external websites that are not provided or maintained by or in any way affiliated with us.

Please note that we do not guarantee the accuracy, relevance, timeliness, or completeness of any information on these external websites.

Errors and Omissions Disclaimer⚓︎

The information given by the Service is for general guidance on matters of interest only. Even if we take every precaution to insure that the content of the Service is both current and accurate, errors can occur. Plus, given the changing nature of laws, rules and regulations, there may be delays, omissions or inaccuracies in the information contained on the Service.

We are not responsible for any errors or omissions, or for the results obtained from the use of this information.

Fair Use Disclaimer⚓︎

We may use copyrighted material which has not always been specifically authorized by the copyright owner. We are making such material available for criticism, comment, news reporting, teaching, scholarship, or research.

We believe this constitutes a "fair use" of any such copyrighted material as provided for in section 107 of the United States Copyright law.

If You wish to use copyrighted material from the Service for your own purposes that go beyond fair use, You must obtain permission from the copyright owner.

Views Expressed Disclaimer⚓︎

The Service may contain views and opinions which are those of the authors and do not necessarily reflect the official policy or position of any other author, agency, organization, employer or company, including us.

Comments published by users are their sole responsibility and the users will take full responsibility, liability and blame for any libel or litigation that results from something written in or as a direct result of something written in a comment. We are not liable for any comment published by users and reserves the right to delete any comment for any reason whatsoever.

No Responsibility Disclaimer⚓︎

The information on the Service is provided with the understanding that we are not herein engaged in rendering legal, accounting, tax, or other professional advice and services. As such, it should not be used as a substitute for consultation with professional accounting, tax, legal or other competent advisers.

In no event shall we be liable for any special, incidental, indirect, or consequential damages whatsoever arising out of or in connection with your access or use or inability to access or use the Service.

"Use at Your Own Risk" Disclaimer⚓︎

All information in the Service is provided "as is", with no guarantee of completeness, accuracy, timeliness or of the results obtained from the use of this information, and without warranty of any kind, express or implied, including, but not limited to warranties of performance, merchantability and fitness for a particular purpose.

We will not be liable to You or anyone else for any decision made or action taken in reliance on the information given by the Service or for any consequential, special or similar damages, even if advised of the possibility of such damages.

Contact Us⚓︎

If you have any questions about this Disclaimer, You can contact Us:

© License⚓︎

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The emoji in the home page is designed by OpenMoji – the open-source emoji and icon project (License: CC BY-SA 4.0) and the favicon is designed by IconPark (License: Apache License 2.0).

CC BY-NC-SA 4.0

Copyright © 2019 - 2023 Yihong Liu
\ No newline at end of file + Introduction - MIS Reading List

Introduction⚓︎

What to read as a Ph.D. student or researcher majoring in Management Information Systems (MIS)?

This reading list covers MIS-relevant journals, conferences, books, papers, and resources.

A considerable number of papers in the list comes from the MIS7420 Seminar in Management Information Systems.

Ordered alphabetically, Prof. Amit Mehra, Atanu Lahiri, Jianqing Chen, Srinivasan Raghunathan, Sumit Sarkar, Syam Menon, Vijay Mookerjee, Zhiqiang Zheng are in charge of this course and contribute a lot to this list.

Thanks to them for creating a diverse topic portfolio in this MIS reading list!

What is MIS?⚓︎

  • Monideepa Tarafdar, Guohou Shan, Jason Bennett Thatcher, Alok Gupta (2022) Intellectual Diversity in IS Research: Discipline-Based Conceptualization and an Illustration from Information Systems Research. Information Systems Research 33(4):1490-1510. source
IS Methods and Theories

Information systems researchers use a bevvy of research methods and theoretical lenses to explore phenomena of interest. The following links will take you to sites that have been developed by members of the IS community who are experts in particular areas.

Theories in IS Research

Design Research

Qualitative Research

Quantitative Research

Spatial Design Support Systems

Research Task Repository

Decision Support Systems

---- AIS - IS Research, Methods, and Theories

✍🏼 About The Author⚓︎

Yihong Liu is a Ph.D. candidate in the Management Science, Information Systems Concentration at the Naveen Jindal School of Management, UT Dallas.

ℹ Disclaimer⚓︎

Click to expand and read the disclaimer.

Last updated: March 08, 2022

Interpretation and Definitions⚓︎

Interpretation⚓︎

The words of which the initial letter is capitalized have meanings defined under the following conditions.

The following definitions shall have the same meaning regardless of whether they appear in singular or in plural.

Definitions⚓︎

For the purposes of this Disclaimer:

  • We (referred to as either "We", "Us" or "Our" in this Disclaimer) refers to the authors of "MIS Reading List".

  • Service refers to the Website.

  • You means the individual accessing the Service, or the company, or other legal entity on behalf of which such individual is accessing or using the Service, as applicable.

  • Website refers to MIS Reading List, accessible from https://liu-yihong.github.io/MISReadingList/

Disclaimer⚓︎

The information contained on the Service is for general information purposes only.

We assume no responsibility for errors or omissions in the contents of the Service.

In no event shall we be liable for any special, direct, indirect, consequential, or incidental damages or any damages whatsoever, whether in an action of contract, negligence or other tort, arising out of or in connection with the use of the Service or the contents of the Service. We reserve the right to make additions, deletions, or modifications to the contents on the Service at any time without prior notice.

We do not warrant that the Service is free of viruses or other harmful components.

The Service may contain links to external websites that are not provided or maintained by or in any way affiliated with us.

Please note that we do not guarantee the accuracy, relevance, timeliness, or completeness of any information on these external websites.

Errors and Omissions Disclaimer⚓︎

The information given by the Service is for general guidance on matters of interest only. Even if we take every precaution to insure that the content of the Service is both current and accurate, errors can occur. Plus, given the changing nature of laws, rules and regulations, there may be delays, omissions or inaccuracies in the information contained on the Service.

We are not responsible for any errors or omissions, or for the results obtained from the use of this information.

Fair Use Disclaimer⚓︎

We may use copyrighted material which has not always been specifically authorized by the copyright owner. We are making such material available for criticism, comment, news reporting, teaching, scholarship, or research.

We believe this constitutes a "fair use" of any such copyrighted material as provided for in section 107 of the United States Copyright law.

If You wish to use copyrighted material from the Service for your own purposes that go beyond fair use, You must obtain permission from the copyright owner.

Views Expressed Disclaimer⚓︎

The Service may contain views and opinions which are those of the authors and do not necessarily reflect the official policy or position of any other author, agency, organization, employer or company, including us.

Comments published by users are their sole responsibility and the users will take full responsibility, liability and blame for any libel or litigation that results from something written in or as a direct result of something written in a comment. We are not liable for any comment published by users and reserves the right to delete any comment for any reason whatsoever.

No Responsibility Disclaimer⚓︎

The information on the Service is provided with the understanding that we are not herein engaged in rendering legal, accounting, tax, or other professional advice and services. As such, it should not be used as a substitute for consultation with professional accounting, tax, legal or other competent advisers.

In no event shall we be liable for any special, incidental, indirect, or consequential damages whatsoever arising out of or in connection with your access or use or inability to access or use the Service.

"Use at Your Own Risk" Disclaimer⚓︎

All information in the Service is provided "as is", with no guarantee of completeness, accuracy, timeliness or of the results obtained from the use of this information, and without warranty of any kind, express or implied, including, but not limited to warranties of performance, merchantability and fitness for a particular purpose.

We will not be liable to You or anyone else for any decision made or action taken in reliance on the information given by the Service or for any consequential, special or similar damages, even if advised of the possibility of such damages.

Contact Us⚓︎

If you have any questions about this Disclaimer, You can contact Us:

© License⚓︎

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The emoji in the home page is designed by OpenMoji – the open-source emoji and icon project (License: CC BY-SA 4.0) and the favicon is designed by IconPark (License: Apache License 2.0).

CC BY-NC-SA 4.0

Copyright © 2019 - 2023 Yihong Liu
\ No newline at end of file diff --git a/jobs/index.html b/jobs/index.html index aeed6c6..c780c4a 100644 --- a/jobs/index.html +++ b/jobs/index.html @@ -1 +1 @@ - Jobs - MIS Reading List

Jobs⚓︎

During the months of July and August, schools will usually post openings for IS positions (note that the exact time frame may vary depending on the field, such as Marketing).

To be prepared for the hiring process, it is important to have all necessary documents ready, including your CV, teaching and research statement, teaching evaluations, recommendation letters, and any required paper copies (requirement lists may vary between schools). Keep in mind to only include projects and papers that you are well-versed in and able to clearly explain in a 2-minute summary in your CV.

You may also want to share your document package with your PhD colleagues.

IS Academic Jobs 2023-2024⚓︎

You find new job position and want to share?

Feel free to submit the job information through this link!

Please note that jobs starting at August 2023 are excluded from this list.

Where to Find IS Jobs⚓︎

IS job openings can often be found at:

  1. Association for Information Systems (AIS) Career Services
  2. INFORMS Career Center
  3. Production and Operations Management Society (POMS) Placement List
  4. Interdisciplinoxy.com
  5. Akadeus.com
  6. FacultyVacancies.com
  7. AcademicJobsOnline
  8. IHE Careers
  9. AOM Career Services
  10. IS Jobs

How IS Jobs Pay⚓︎

OpenPayrolls provides nationwide public salary database for federal agencies, states, counties, cities, universities, colleges, and K-12 schools.

States Limiting Tenure⚓︎

  1. highereddive, "5 state (Texas, North Dakota, Louisiana, Florida, Iowa) plans to restrict faculty tenure you’ll want to watch"
  2. bestcolleges, "Tenure Under Attack Nationwide(South Carolina, Georgia, Iowa)"
  3. universityworldnews, "Tenure is under attack in the US and your country is next"
  4. thehill, "Texas and Florida take steps to limit professor tenure at state schools"
  5. ncnewsline, "New bill targets tenure, calls for scrutiny of research at UNC System campuses, community colleges"
  6. University of Tennessee, "Periodic Post-Tenure Performance Reviews | Office of the Provost"

Last Update On 2023-06-17.

Copyright © 2019 - 2023 Yihong Liu
\ No newline at end of file + Jobs - MIS Reading List

Jobs⚓︎

During the months of July and August, schools will usually post openings for IS positions (note that the exact time frame may vary depending on the field, such as Marketing).

To be prepared for the hiring process, it is important to have all necessary documents ready, including your CV, teaching and research statement, teaching evaluations, recommendation letters, and any required paper copies (requirement lists may vary between schools). Keep in mind to only include projects and papers that you are well-versed in and able to clearly explain in a 2-minute summary in your CV.

You may also want to share your document package with your PhD colleagues.

IS Academic Jobs 2023-2024⚓︎

You find new job position and want to share?

Feel free to submit the job information through this link!

Please note that jobs starting at August 2023 are excluded from this list.

Where to Find IS Jobs⚓︎

IS job openings can often be found at:

  1. Association for Information Systems (AIS) Career Services
  2. INFORMS Career Center
  3. INFORMS Connect - Available Positions Community (Login Required)
  4. Production and Operations Management Society (POMS) Placement List
  5. Interdisciplinoxy.com
  6. Akadeus.com
  7. FacultyVacancies.com
  8. AcademicJobsOnline
  9. IHE Careers
  10. AOM Career Services
  11. IS Jobs

How IS Jobs Pay⚓︎

OpenPayrolls provides nationwide public salary database for federal agencies, states, counties, cities, universities, colleges, and K-12 schools.

States Limiting Tenure⚓︎

  1. highereddive, "5 state (Texas, North Dakota, Louisiana, Florida, Iowa) plans to restrict faculty tenure you’ll want to watch"
  2. bestcolleges, "Tenure Under Attack Nationwide(South Carolina, Georgia, Iowa)"
  3. universityworldnews, "Tenure is under attack in the US and your country is next"
  4. thehill, "Texas and Florida take steps to limit professor tenure at state schools"
  5. ncnewsline, "New bill targets tenure, calls for scrutiny of research at UNC System campuses, community colleges"
  6. University of Tennessee, "Periodic Post-Tenure Performance Reviews | Office of the Provost"

Last Update On 2023-07-12.

Copyright © 2019 - 2023 Yihong Liu
\ No newline at end of file diff --git a/journals/index.html b/journals/index.html index e95e3d7..c9b1644 100644 --- a/journals/index.html +++ b/journals/index.html @@ -1 +1 @@ - Journals - MIS Reading List

MIS Journals⚓︎

Information Systems Research (ISR)

Information Systems Research (ISR) is an author-friendly peer-reviewed journal that publishes the best research in the information systems discipline. Its mission is to advance knowledge about the effective and efficient utilization of information technology by individuals, groups, organizations, society, and nations for the improvement of economic and social welfare.

The journal covers a wide variety of phenomena and topics related to the design, management, use, valuation, and impact of information technologies at different levels of analysis. ISR publishes research that examines topics from a wide range of research traditions including cognitive psychology, economics, computer science, operations research, design science, organization theory, organization behavior, sociology, and strategic management.

---- INFORMS - Information Systems Research / RSS Feed

Management Science (MS)

Management Science (MS) is a scholarly journal that publishes scientific research on the practice of management focusing on the problems, interest, and concerns of managers.

Within its scope are all aspects of management related to strategy, entrepreneurship, innovation, information technology, and organizations as well as all functional areas of business, such as accounting, finance, marketing, and operations.

---- INFORMS - Management Science / RSS Feed

MIS Quarterly (MISQ)

The MIS Quarterly’s trifecta vision is to

(1) achieve impact on scholarship and practice as the leading source of novel and accreted IS knowledge,

(2) exhibit range in work published with respect to problem domains and stakeholders addressed as well as theoretical and methodological approaches used, and

(3) execute effective editorial processes in a timely manner.

---- MIS Quarterly / Unofficial RSS Feed

AIS - Senior Scholars' List of Premier Journals

The College of Senior Scholars encourages colleagues, as well as deans and department chairs, to treat a list of premier journals as the top journals in our field. Such a list is intended to provide more consistency and meaningfulness to tenure and promotion cases.

The journal list is limited to those in the "IS field," and omits both multidisciplinary outlets and specialty areas. Nevertheless, the list recognizes topical, methodological, and geographical diversity. In addition, the review processes are stringent, editorial board members are widely-respected and recognized, and there is international readership and contribution.

The journals in the list are, in alphabetical order:

Decision Support Systems

European Journal of Information Systems

Information & Management

Information and Organization

Information Systems Journal

Information Systems Research

Journal of the AIS

Journal of Information Technology

Journal of MIS

Journal of Strategic Information Systems

MIS Quarterly

---- Senior Scholars' List of Premier Journals / Rankings of universities and authors based on the Senior Scholars' Basket of Journals

UTD24

The UT Dallas’ Naveen Jindal School of Management has created a database to track publications in 24 leading business journals.

The database contains titles and author affiliations of papers published in these journals since 1990.

The information in the database is used to provide the top 100 business school rankings since 1990 based on the total contributions of faculty.

---- UTD24

FT50

The Financial Times conducted a review in May 2016 of the journals that count towards its research rank. As a result, the number of journals considered went up to 50 compared to 45 previously.

---- FT50 / archive

Other Journals⚓︎

© License⚓︎

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Last Update On 2023-03-03.

Copyright © 2019 - 2023 Yihong Liu
\ No newline at end of file + Journals - MIS Reading List

MIS Journals⚓︎

Information Systems Research (ISR)

Information Systems Research (ISR) is an author-friendly peer-reviewed journal that publishes the best research in the information systems discipline. Its mission is to advance knowledge about the effective and efficient utilization of information technology by individuals, groups, organizations, society, and nations for the improvement of economic and social welfare.

The journal covers a wide variety of phenomena and topics related to the design, management, use, valuation, and impact of information technologies at different levels of analysis. ISR publishes research that examines topics from a wide range of research traditions including cognitive psychology, economics, computer science, operations research, design science, organization theory, organization behavior, sociology, and strategic management.

---- INFORMS - Information Systems Research / RSS Feed

Management Science (MS)

Management Science (MS) is a scholarly journal that publishes scientific research on the practice of management focusing on the problems, interest, and concerns of managers.

Within its scope are all aspects of management related to strategy, entrepreneurship, innovation, information technology, and organizations as well as all functional areas of business, such as accounting, finance, marketing, and operations.

---- INFORMS - Management Science / RSS Feed

MIS Quarterly (MISQ)

The MIS Quarterly’s trifecta vision is to

(1) achieve impact on scholarship and practice as the leading source of novel and accreted IS knowledge,

(2) exhibit range in work published with respect to problem domains and stakeholders addressed as well as theoretical and methodological approaches used, and

(3) execute effective editorial processes in a timely manner.

---- MIS Quarterly / Unofficial RSS Feed

AIS - Senior Scholars' List of Premier Journals

The College of Senior Scholars encourages colleagues, as well as deans and department chairs, to treat a list of premier journals as the top journals in our field. Such a list is intended to provide more consistency and meaningfulness to tenure and promotion cases.

The journal list is limited to those in the "IS field," and omits both multidisciplinary outlets and specialty areas. Nevertheless, the list recognizes topical, methodological, and geographical diversity. In addition, the review processes are stringent, editorial board members are widely-respected and recognized, and there is international readership and contribution.

The journals in the list are, in alphabetical order:

Decision Support Systems

European Journal of Information Systems

Information & Management

Information and Organization

Information Systems Journal

Information Systems Research

Journal of the AIS

Journal of Information Technology

Journal of MIS

Journal of Strategic Information Systems

MIS Quarterly

---- Senior Scholars' List of Premier Journals / Rankings of universities and authors based on the Senior Scholars' Basket of Journals

UTD24

The UT Dallas’ Naveen Jindal School of Management has created a database to track publications in 24 leading business journals.

The database contains titles and author affiliations of papers published in these journals since 1990.

The information in the database is used to provide the top 100 business school rankings since 1990 based on the total contributions of faculty.

---- UTD24

FT50

The Financial Times conducted a review in May 2016 of the journals that count towards its research rank. As a result, the number of journals considered went up to 50 compared to 45 previously.

---- FT50 / archive

Other Journals⚓︎

© License⚓︎

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Last Update On 2023-03-03.

Copyright © 2019 - 2023 Yihong Liu
\ No newline at end of file diff --git a/references/index.html b/references/index.html index 08bfdb0..21d6251 100644 --- a/references/index.html +++ b/references/index.html @@ -1 +1 @@ - Reference Books, Articles & Resources - MIS Reading List

Reference Books & Papers⚓︎

The Academic Life⚓︎

Advice & Tips⚓︎

  • Richard Hamming, "You and Your Research", 1986. source archive
  • Peters, Robert L. Getting what you came for: the smart student's guide to earning a master's or a Ph. D. New York: Farrar, Straus and Giroux, 1997. source
  • Booth, W. C., Booth, W. C., Colomb, G. G., Colomb, G. G., Williams, J. M., & Williams, J. M. (2003). "The craft of research". University of Chicago press. source
  • Fei-Fei Li, "De-Mystifying Good Research and Good Papers", 2009. source archive
  • Feibelman, Peter J. "A PhD is not enough!: A guide to survival in science". Basic Books, 2011.
  • Phillips, Estelle, and Derek Pugh. "How to Get a PhD: A Handbook for Students and their Supervisors". McGraw-Hill Education (UK), 2015.
  • Andrej Karpathy, "A Survival Guide to a PhD", 2016. source archive
  • Volkan Cirik, "PhD 101", 2019. source archive
  • Sam Altman, "How To Be Successful", 2019. source archive
  • Sebastian Ruder, "10 Tips for Research and a PhD", 2020. source archive
  • Isabelle Augenstein, "Increasing Well-Being in Academia", 2020. source archive
  • Li, Longxing. "The A-Z of the PhD Trajectory: A Practical Guide for a Successful Journey." International Journal of Teaching and Learning in Higher Education 32.3 (2020): 536-538. source
  • Banafsheh Behzad and Xiaonan (Shannon) Shang, "Transitioning from Student to Professional" INFORMS Speakers Program, 2022. video

Academic Writings⚓︎

  • Cochrane, John H. "Writing Tips for Ph. D. Students." Chicago, IL: University of Chicago, 2005. pdf Chinese Version
  • Zinsser, William. "On writing well: The classic guide to writing nonfiction." New York, NY (2006). source
  • Clark, R. P. (2008). "Writing tools: 55 essential strategies for every writer". Little, Brown Spark. source
  • McCarthy, Michael, and Felicity O'dell. Academic vocabulary in use. Ernst Klett Sprachen, 2008.
  • Sword, H. (2012)."Stylish academic writing". Harvard University Press. source
  • Brittman, Felicia. "The Most Common Habits from more than 200 English Papers written by Graduate Chinese Engineering Students." (2011). pdf
  • Swales, J.M. et al. "Academic Writing for Graduate Students: Essential Tasks and Skills". University of Michigan Press, 2012. source
  • Clark, Roy Peter. How to write short: Word craft for fast times. Little, Brown Spark, 2013. source
  • Bailey, Stephen. Academic writing: A handbook for international students. Routledge, 2014. source
  • Morley, John. "Academic phrasebank." Manchester: University of Manchester (2014). source
  • Wallwork, A. (2016). "English for writing research papers". Springer. source
  • Card, Stuart K. "The PhD Thesis Deconstructed." IEEE Computer Graphics and Applications 36.04 (2016): 92-101. source
  • Silvia, Paul J. How to write a lot: A practical guide to productive academic writing. American Psychological Association, 2018. source
  • INFOGRAPHIC: The secret to using tenses in scientific writing source
  • Using tenses in scientific writing: Tense considerations for science writing pdf
  • Struijk, Mylène, et al. "Putting the IS back into IS research." Information Systems Journal (2021). source

Review Writings⚓︎

  • Elisabeth PainSep. 22, 2. (2016, September 22). "How to review a paper". Retrieved August 31, 2020. source archive
  • Rai, A. (2016). Editor's comments: writing a virtuous review. MIS Quarterly, 40(3), iii-x. pdf
  • Wiley. "How to perform a peer review". Retrieved August 31, 2020. source
  • Indiana University East. (2017). "How to Write a Review of a Scholarly Article". Retrieved August 31, 2020. source
  • Adams, J. (2020, August 6). "How to Write an Article Review". source
  • Ahmed, B. S. (2018, February 26). "Tips and advice when you review a scientific paper". Elsevier. source
  • MIS Quarterly. (2020). "Reviewing for MIS Quarterly: Virtuous Reviewing at MIS Quarterly". source.

Response to Reviews⚓︎

  • Pang, Min-Seok and Thatcher, Jason B. (2023) "A Practical Guide for Successful Revisions and Engagements with Reviewers," Journal of the Association for Information Systems, 24(2), 317-327. source

Academic Presentations⚓︎

  • Reinhart, Susan M. Giving academic presentations. Ann Arbor, MI: University of Michigan Press, 2002. source
  • Chivers, Barbara, and Michael Shoolbred. A Student′ s Guide to Presentations: Making your Presentation Count. Sage, 2007. source
  • Graham Burton. Presenting: Deliver Academic Presentations with Confidence HarperCollins UK, 2014.
  • Rendle-Short, Johanna. The academic presentation: Situated talk in action. Routledge, 2016.
  • Nycyk, Michael. "Academic and scientific poster presentation: a modern comprehensive guide." (2018): 1550-1552.
  • Guest, Michael. Conferencing and Presentation English for Young Academic. Springer, 2018. source

Academic Career⚓︎

  • Showalter, English. The MLA guide to the job search: A handbook for departments and for PhDs and PhD candidates in English and foreign languages. Modern Language Assoc. of America, 1996.
  • Goldsmith, John A., John Komlos, and Penny Schine Gold. The Chicago guide to your academic career: A portable mentor for scholars from graduate school through tenure. University of Chicago Press, 2001.
  • Kelsky, Karen. The professor is in: The essential guide to turning your Ph. D. into a job. Crown, 2015. homepage
  • Vick, Julia Miller, Jennifer S. Furlong, and Rosanne Lurie. "The academic job search handbook." The Academic Job Search Handbook. University of Pennsylvania Press, 2016.
  • Boice, Robert. Advice for new faculty members. Vol. 75. Needham Heights, MA: Allyn & Bacon, 2000.
  • Firth, David, Matt Germonprez, and Jason Thatcher. "Managing your PhD student career: How to prepare for the job market." Communications of the Association for Information Systems 34.1 (2014): 5. source
  • Liu, Yihong. "Notes for Ph.D. Job Interview Experiences 02–21-2020." Yihong Liu’s Blog, 26 May 2020, source.
  • Baquero, Carlos. "Publishing, The Choice and The Luck." blog@CACM | Communications of the ACM, Communications of the ACM, 22 Nov. 2021, source archive.
  • Baquero, Carlos. "Picking Publication Targets." March 2022 | Communications of the ACM, Communications of the ACM, 1 Mar. 2022, source archive.
  • Association for Information Systems (AIS) Career Services
  • INFORMS Career Center
  • Production and Operations Management Society (POMS) Placement List
  • Interdisciplinoxy.com
  • Akadeus.com

Teaching⚓︎

  • Bain, Ken. What the best college teachers do. Harvard University Press, 2004. source
  • Filene, Peter. The joy of teaching: A practical guide for new college instructors. Univ of North Carolina Press, 2009. source
  • Seldin, Peter, J. Elizabeth Miller, and Clement A. Seldin. The teaching portfolio: A practical guide to improved performance and promotion/tenure decisions. John Wiley & Sons, 2010. source
  • Colby, Anne, et al. Rethinking undergraduate business education: Liberal learning for the profession. John Wiley & Sons, 2011. source
  • Bowen, José Antonio. Teaching naked: How moving technology out of your college classroom will improve student learning. John Wiley & Sons, 2012.
  • Angelo, Thomas A., and K. Patricia Cross. Classroom Assessment Techniques: A Handbook for College Teachers. Jossey Bass Wiley, 2012. source
  • Lang, James M. Cheating Lessons: Learning from Academic Dishonesty. Harvard University Press, 2013. source
  • Doyle, Elaine, Patrick Buckley, and Conor Carroll, eds. Innovative business school teaching: Engaging the millennial generation. Routledge, 2014. source
  • David Gooblar, "They Haven’t Done the Reading. Again.", 2014. archive
  • Carey, Benedict. How we learn: The surprising truth about when, where, and why it happens. Random House Trade Paperbacks, 2015.
  • Nilson, Linda B. Specifications grading: Restoring rigor, motivating students, and saving faculty time. Stylus Publishing, LLC, 2015. source
  • Henderson, Linda J. "Start Talking: A Handbook for Engaging Difficult Dialogues in Higher Education." (2016): 56-60. source
  • Nilson, Linda B. Teaching at its best: A research-based resource for college instructors. John Wiley & Sons, 2016. source
  • Howard, Jay. "Class Discussion: From Blank Stares to True Engagement.", 2019. source archive
  • Gilmore, Joanna, and Molly Hatcher, eds. Preparing for College and University Teaching: Competencies for Graduate and Professional Students. Stylus Publishing, LLC, 2021. source
  • Müller, S. D. (2022). Student Research as Legitimate Peripheral Participation. Communications of the Association for Information Systems, 50, pp-pp. source
  • Zheng, Lily. DEI Deconstructed: Your No-nonsense Guide to Doing the Work and Doing it Right. Berrett-Koehler Publishers, 2022. source
  • Daniel T. Willingham. Outsmart Your Brain: Why Learning is Hard and How You Can Make It Easy. Gallery Books, 2023. source
  • Regan A. R. Gurung and John Dunlosky. Study Like a Champ: The Psychology-Based Guide to “Grade A” Study Habits. APA LifeTools, 2023. source
  • Barbeau, Lauren, and Claudia Cornejo Happel. "Critical Teaching Behaviors: Defining, Documenting, and Discussing Good Teaching." (2023). source
  • The website "Solve a Teaching Problem" by Eberly Center, Carnegie Mellon University provides practical strategies to address teaching problems across the disciplines.
  • Journal of Management Education
  • Management Teaching Review
  • Journal of Teaching in International Business
  • Journal of Education for Business

AI & Education⚓︎

This list comes from the Center of Teaching and Learning at the University of Texas at Dallas along with other sources.

  • Julia Staffel, ChatGPT and Its Impact on Teaching Philosophy and Other Subjects video
  • Cynthia Alby, Chatgpt: Understanding the New Landscape and Short-Term Solutions Google Docs
  • Lee Skallerup Bessette's Zotero Library on ChatGPT source
  • Teachers On Fire, Should Schools BAN ChatGPT? 4 Reasons Not To! video
  • Eric Prochaska, Embrace the Bot: Designing Writing Assignments in the Face of AI source
  • Alexandra Mihai, Let's get off the fear carousel! source
  • Art Brownlow, AI Essay Writing: Dawn in the Garden of Good and Evil video
  • Kritik Education, 12 Ways Instructors Can Use OpenAI to Transform Assessments source
  • Derek Bruff, A Bigger, Badder Clippy: Enhancing Student Learning with AI Writing Tools source
  • @herfteducator, A Teacher’s Prompt Guide to ChatGPT Aligned With 'What Works Best' pdf
  • Center for Teaching & Assessment of Learning @ University of Delaware, Considerations for Using and Addressing Advanced Automated Tools in Coursework and Assignments website
  • Gabby Jones / Bloomberg, ChatGPT Is a Wake-up Call to Revamp How We Teach Writing website
  • Joshua Wilson, Writing Without Thinking? There’s a Place for ChatGPT — If Used Properly website
  • turnitin.com, AI-generated text: What educators are saying source
  • turnitin.com, AI-generated text: An annotated hotlist for educators source
  • turnitin.com, Guide for approaching AI-generated text in your classroom source
  • SAN JOSÉ STATE UNIVERSITY, Generative AI & ChatGPT: Resources for Instructors source
  • UCLA, ChatGPT and AI Resources source
  • Lance Eaton, Classroom Policies for AI Generative Tools source

Ethics⚓︎

  • Davison, Robert M., Maris G. Martinsons, and Louie HM Wong. "The ethics of action research participation." Information Systems Journal (2021). source
  • Umphress, Elizabeth E., et al. "From the Editors: Insights on how we try to show empathy, respect, and inclusion in AMJ." Academy of Management Journal (2022). source

Dress Code⚓︎

  • Lee, Christopher. "Dressing the Professor: What to Wear for Working in Academia." Gentleman's Gazette, 8 Nov. 2018. source archive
  • Block, Marta Segal. "What to Wear on Campus." HigherEdJobs, 18 Apr. 2017. source archive
  • martinkich. "Student and Faculty Dress Codes." ACADEME BLOG, 5 Feb. 2015. source archive
  • Smart, Michael. "How a Professor Should Dress: Tips for Lecturers, Tas & Teachers." LearnPar, 21 May 202. source archive
  • Lightstone, Karen, Rob Francis, and Lucie Kocum. "University faculty style of dress and students' perception of instructor credibility." International Journal of Business and Social Science 2.15 (2011). source
  • 40+Style. "What to Wear to a Conference or Presentation to Be Stylish and Professional." 40+ Style, 3 Aug. 2020. source
  • Crestline. "What to Wear to a Conference: The Ultimate Guide." Crestline, 28 Feb. 2022. source
  • Monus, Elle. "3 Ways to Dress for a Conference." WikiHow, WikiHow, 10 Oct. 2021. source

Statistics and Probability⚓︎

  • Casella, George, and Roger L. Berger. Statistical inference. Vol. 2. Pacific Grove, CA: Duxbury, 2002. source

Optimization⚓︎

  • Boyd, Stephen, Stephen P. Boyd, and Lieven Vandenberghe. Convex optimization. Cambridge university press, 2004. pdf
  • Sundaram, Rangarajan K. A first course in optimization theory. Cambridge university press, 1996.

Bayesian Optimization⚓︎

  • Brochu, Eric, Vlad M. Cora, and Nando De Freitas. "A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning." arXiv preprint arXiv:1012.2599 (2010). source
  • Shahriari, Bobak, et al. "Taking the human out of the loop: A review of Bayesian optimization." Proceedings of the IEEE 104.1 (2015): 148-175. source
  • Frazier, Peter I. "Bayesian optimization." Recent advances in optimization and modeling of contemporary problems. Informs, 2018. 255-278. source

Microeconomic⚓︎

  • Varian, Hal R. Microeconomic analysis. WW Norton, 1992.
  • Rubinstein, Ariel. Lecture notes in microeconomic theory: the economic agent. Princeton University Press, 2012. pdf

Econometrics⚓︎

  • Greene, William H. Econometric analysis (Eight Edition). (2017).
  • Cameron, A. Colin, and Pravin K. Trivedi. Microeconometrics: methods and applications. Cambridge university press, 2005.
  • Wooldridge, Jeffrey M. Econometric analysis of cross section and panel data. MIT press, 2010.
  • Wooldridge, Jeffrey M. Introductory econometrics: A modern approach. Nelson Education, 2016.
  • Hill, R. Carter, William E. Griffiths, and Guay C. Lim. Principles of econometrics. John Wiley & Sons, 2018.
  • Davidson, Russell, and James G. MacKinnon. Econometric theory and methods. Vol. 5. New York: Oxford University Press, 2004.
  • Maddala, Gangadharrao S. Limited-dependent and qualitative variables in econometrics. No. 3. Cambridge university press, 1986.
  • Angrist, Joshua D., and Jörn-Steffen Pischke. Mostly harmless econometrics: An empiricist's companion. Princeton university press, 2008.
  • Baltagi, Badi Hani. "Econometric analysis of panel data". Springer International Publishing, (6th Edition, 2021). source
  • This Wikipedia page compares technical information for a number of statistical analysis packages.

Causal Inference⚓︎

  • Pearl, Judea. "Causal inference in statistics: An overview." Statistics surveys 3 (2009): 96-146. pdf
  • Pearl, Judea. Causality. Cambridge university press, 2009. author's website
  • Hernán, Miguel A., and James M. Robins. "Causal inference." (2010): 2. source
  • Glymour, Madelyn, Judea Pearl, and Nicholas P. Jewell. Causal inference in statistics: A primer. John Wiley & Sons, 2016. author's website
  • Peters, Jonas, Dominik Janzing, and Bernhard Schölkopf. Elements of causal inference: foundations and learning algorithms. The MIT Press, 2017. source
  • Pearl, Judea, and Dana Mackenzie. The book of why: the new science of cause and effect. Basic books, 2018. author's website
  • Yao, Liuyi, et al. "A survey on causal inference." arXiv preprint arXiv:2002.02770 (2020). source
  • Cunningham, Scott. "Causal inference." Causal Inference. Yale University Press, 2021. author's website
  • Matheus Facure's handbook Causal Inference for The Brave and True github repository
  • Brady Neal's blog provides a good list of books
  • Brady Neal's blog also provides a good course "Introduction to Causal Inference"

Game Theory⚓︎

  • Gibbons, Robert S. Game theory for applied economists. Princeton University Press, 1992.
  • Fudenberg, Drew, and Jean Tirole. Game theory. MIT press, 1991.
  • Myerson, Roger B. Game Theory: Analysis of Conflict. Harvard university press, 2013.
  • Osborne, Martin J., and Ariel Rubinstein. A course in game theory. MIT press, 1994.

Industry Organization⚓︎

  • Tirole, Jean. The theory of industrial organization. MIT press, 1988.
  • Vives, Xavier. Oligopoly pricing: old ideas and new tools. MIT press, 1999.
  • Martin, Stephen. Advanced industrial economics. Blackwell Publishers, 2002.
  • Belleflamme, Paul, and Martin Peitz. Industrial organization: markets and strategies. Cambridge University Press, 2015.

Artificial Intelligence⚓︎

  • Shmueli, Galit. "To explain or to predict?." Statistical science 25.3 (2010): 289-310. source
  • Shmueli, Galit, and Otto R. Koppius. "Predictive analytics in information systems research." MIS quarterly (2011): 553-572. source

Machine Learning⚓︎

  • Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media, 2009. source
  • Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006. pdf
  • Barber, David. Bayesian reasoning and machine learning. Cambridge University Press, 2012. source
  • Murphy, Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.
  • Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep learning." Cambridge: MIT press, 2016. source
  • Sergey Levine provides a course CS W182 / 282A - UC Berkeley at Designing, Visualizing and Understanding Deep Neural Networks
  • Murphy, Kevin P. Probabilistic machine learning: an introduction. MIT press, 2022. website
  • Murphy, Kevin P. Probabilistic machine learning: Advanced Topics. MIT press, 2023. website
  • Berente, Nicholas, et al. "Managing artificial intelligence." MIS Quarterly 45.3 (2021): 1433-1450. source
  • Balaji Padmanabhan, Xiao Fang, Nachiketa Sahoo, and Andrew Burton-Jones. "Machine Learning in Information Systems Research", MIS Quarterly Editors' Comments, 2022. source

Few-shot Learning⚓︎

  • Wang, Yaqing, et al. "Generalizing from a few examples: A survey on few-shot learning." ACM Computing Surveys (CSUR) 53.3 (2020): 1-34. source

Transfer Learning⚓︎

  • Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359. source
  • Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. "A survey of transfer learning." Journal of Big data 3.1 (2016): 1-40. source
  • Tan, Chuanqi, et al. "A survey on deep transfer learning." International conference on artificial neural networks. Springer, Cham, 2018. source
  • Zhuang, Fuzhen, et al. "A comprehensive survey on transfer learning." Proceedings of the IEEE 109.1 (2020): 43-76. source

Reinforcement Learning⚓︎

Computer Vision⚓︎

Natural Language Processing⚓︎

  • keon on github.com provides a curated list dedicated to natural language processing at awesome-nlp

Stochastic Differential Equation⚓︎

  • Gardiner, Crispin W. "Handbook of stochastic methods: for physics, chemistry and the natural sciences." (2004). source
  • Dixit, Robert K., and Robert S. Pindyck. "Investment under uncertainty". Princeton university press, 2012. source
  • Klebaner, Fima C. "Introduction to stochastic calculus with applications". World Scientific Publishing Company, 2012. source
  • Evans, Lawrence C. "An introduction to stochastic differential equations". Vol. 82. American Mathematical Soc., 2012. pdf
  • Mikosch, Thomas. "Elementary stochastic calculus with finance in view". World scientific, 1998. source
  • Oksendal, Bernt. "Stochastic differential equations: an introduction with applications". Springer Science & Business Media, 2013. source
  • Mao, Xuerong. "Stochastic differential equations and applications". Elsevier, 2007. source

Theory Building⚓︎

  • Hassan, Nik Rushdi; Lowry, Paul Benjamin; and Mathiassen, Lars (2022) "Useful Products in Information Systems Theorizing: A Discursive Formation Perspective," Journal of the Association for Information Systems, 23(2), 418-446. source

Code Analysis⚓︎

© License⚓︎

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Last Update On 2023-06-02.

Copyright © 2019 - 2023 Yihong Liu
\ No newline at end of file + Reference Books, Articles & Resources - MIS Reading List

Reference Books & Papers⚓︎

The Academic Life⚓︎

Advice & Tips⚓︎

  • Richard Hamming, "You and Your Research", 1986. source archive
  • Peters, Robert L. Getting what you came for: the smart student's guide to earning a master's or a Ph. D. New York: Farrar, Straus and Giroux, 1997. source
  • Booth, W. C., Booth, W. C., Colomb, G. G., Colomb, G. G., Williams, J. M., & Williams, J. M. (2003). "The craft of research". University of Chicago press. source
  • Fei-Fei Li, "De-Mystifying Good Research and Good Papers", 2009. source archive
  • Feibelman, Peter J. "A PhD is not enough!: A guide to survival in science". Basic Books, 2011.
  • Phillips, Estelle, and Derek Pugh. "How to Get a PhD: A Handbook for Students and their Supervisors". McGraw-Hill Education (UK), 2015.
  • Andrej Karpathy, "A Survival Guide to a PhD", 2016. source archive
  • Volkan Cirik, "PhD 101", 2019. source archive
  • Sam Altman, "How To Be Successful", 2019. source archive
  • Sebastian Ruder, "10 Tips for Research and a PhD", 2020. source archive
  • Isabelle Augenstein, "Increasing Well-Being in Academia", 2020. source archive
  • Li, Longxing. "The A-Z of the PhD Trajectory: A Practical Guide for a Successful Journey." International Journal of Teaching and Learning in Higher Education 32.3 (2020): 536-538. source
  • Banafsheh Behzad and Xiaonan (Shannon) Shang, "Transitioning from Student to Professional" INFORMS Speakers Program, 2022. video

Academic Writings⚓︎

  • Cochrane, John H. "Writing Tips for Ph. D. Students." Chicago, IL: University of Chicago, 2005. pdf Chinese Version
  • Zinsser, William. "On writing well: The classic guide to writing nonfiction." New York, NY (2006). source
  • Clark, R. P. (2008). "Writing tools: 55 essential strategies for every writer". Little, Brown Spark. source
  • McCarthy, Michael, and Felicity O'dell. Academic vocabulary in use. Ernst Klett Sprachen, 2008.
  • Sword, H. (2012)."Stylish academic writing". Harvard University Press. source
  • Brittman, Felicia. "The Most Common Habits from more than 200 English Papers written by Graduate Chinese Engineering Students." (2011). pdf
  • Swales, J.M. et al. "Academic Writing for Graduate Students: Essential Tasks and Skills". University of Michigan Press, 2012. source
  • Clark, Roy Peter. How to write short: Word craft for fast times. Little, Brown Spark, 2013. source
  • Bailey, Stephen. Academic writing: A handbook for international students. Routledge, 2014. source
  • Morley, John. "Academic phrasebank." Manchester: University of Manchester (2014). source
  • Wallwork, A. (2016). "English for writing research papers". Springer. source
  • Card, Stuart K. "The PhD Thesis Deconstructed." IEEE Computer Graphics and Applications 36.04 (2016): 92-101. source
  • Silvia, Paul J. How to write a lot: A practical guide to productive academic writing. American Psychological Association, 2018. source
  • INFOGRAPHIC: The secret to using tenses in scientific writing source
  • Using tenses in scientific writing: Tense considerations for science writing pdf
  • Struijk, Mylène, et al. "Putting the IS back into IS research." Information Systems Journal (2021). source

Review Writings⚓︎

  • Elisabeth PainSep. 22, 2. (2016, September 22). "How to review a paper". Retrieved August 31, 2020. source archive
  • Rai, A. (2016). Editor's comments: writing a virtuous review. MIS Quarterly, 40(3), iii-x. pdf
  • Wiley. "How to perform a peer review". Retrieved August 31, 2020. source
  • Indiana University East. (2017). "How to Write a Review of a Scholarly Article". Retrieved August 31, 2020. source
  • Adams, J. (2020, August 6). "How to Write an Article Review". source
  • Ahmed, B. S. (2018, February 26). "Tips and advice when you review a scientific paper". Elsevier. source
  • MIS Quarterly. (2020). "Reviewing for MIS Quarterly: Virtuous Reviewing at MIS Quarterly". source.

Response to Reviews⚓︎

  • Pang, Min-Seok and Thatcher, Jason B. (2023) "A Practical Guide for Successful Revisions and Engagements with Reviewers," Journal of the Association for Information Systems, 24(2), 317-327. source

Academic Presentations⚓︎

  • Reinhart, Susan M. Giving academic presentations. Ann Arbor, MI: University of Michigan Press, 2002. source
  • Chivers, Barbara, and Michael Shoolbred. A Student′ s Guide to Presentations: Making your Presentation Count. Sage, 2007. source
  • Graham Burton. Presenting: Deliver Academic Presentations with Confidence HarperCollins UK, 2014.
  • Rendle-Short, Johanna. The academic presentation: Situated talk in action. Routledge, 2016.
  • Nycyk, Michael. "Academic and scientific poster presentation: a modern comprehensive guide." (2018): 1550-1552.
  • Guest, Michael. Conferencing and Presentation English for Young Academic. Springer, 2018. source

Academic Career⚓︎

  • Showalter, English. The MLA guide to the job search: A handbook for departments and for PhDs and PhD candidates in English and foreign languages. Modern Language Assoc. of America, 1996.
  • Goldsmith, John A., John Komlos, and Penny Schine Gold. The Chicago guide to your academic career: A portable mentor for scholars from graduate school through tenure. University of Chicago Press, 2001.
  • Kelsky, Karen. The professor is in: The essential guide to turning your Ph. D. into a job. Crown, 2015. homepage
  • Vick, Julia Miller, Jennifer S. Furlong, and Rosanne Lurie. "The academic job search handbook." The Academic Job Search Handbook. University of Pennsylvania Press, 2016.
  • Boice, Robert. Advice for new faculty members. Vol. 75. Needham Heights, MA: Allyn & Bacon, 2000.
  • Firth, David, Matt Germonprez, and Jason Thatcher. "Managing your PhD student career: How to prepare for the job market." Communications of the Association for Information Systems 34.1 (2014): 5. source
  • Liu, Yihong. "Notes for Ph.D. Job Interview Experiences 02–21-2020." Yihong Liu’s Blog, 26 May 2020, source.
  • Baquero, Carlos. "Publishing, The Choice and The Luck." blog@CACM | Communications of the ACM, Communications of the ACM, 22 Nov. 2021, source archive.
  • Baquero, Carlos. "Picking Publication Targets." March 2022 | Communications of the ACM, Communications of the ACM, 1 Mar. 2022, source archive.
  • Association for Information Systems (AIS) Career Services
  • INFORMS Career Center
  • Production and Operations Management Society (POMS) Placement List
  • Interdisciplinoxy.com
  • Akadeus.com

Teaching⚓︎

  • Bain, Ken. What the best college teachers do. Harvard University Press, 2004. source
  • Filene, Peter. The joy of teaching: A practical guide for new college instructors. Univ of North Carolina Press, 2009. source
  • Seldin, Peter, J. Elizabeth Miller, and Clement A. Seldin. The teaching portfolio: A practical guide to improved performance and promotion/tenure decisions. John Wiley & Sons, 2010. source
  • Colby, Anne, et al. Rethinking undergraduate business education: Liberal learning for the profession. John Wiley & Sons, 2011. source
  • Bowen, José Antonio. Teaching naked: How moving technology out of your college classroom will improve student learning. John Wiley & Sons, 2012.
  • Angelo, Thomas A., and K. Patricia Cross. Classroom Assessment Techniques: A Handbook for College Teachers. Jossey Bass Wiley, 2012. source
  • Lang, James M. Cheating Lessons: Learning from Academic Dishonesty. Harvard University Press, 2013. source
  • Doyle, Elaine, Patrick Buckley, and Conor Carroll, eds. Innovative business school teaching: Engaging the millennial generation. Routledge, 2014. source
  • David Gooblar, "They Haven’t Done the Reading. Again.", 2014. archive
  • Carey, Benedict. How we learn: The surprising truth about when, where, and why it happens. Random House Trade Paperbacks, 2015.
  • Nilson, Linda B. Specifications grading: Restoring rigor, motivating students, and saving faculty time. Stylus Publishing, LLC, 2015. source
  • Henderson, Linda J. "Start Talking: A Handbook for Engaging Difficult Dialogues in Higher Education." (2016): 56-60. source
  • Nilson, Linda B. Teaching at its best: A research-based resource for college instructors. John Wiley & Sons, 2016. source
  • Howard, Jay. "Class Discussion: From Blank Stares to True Engagement.", 2019. source archive
  • Gilmore, Joanna, and Molly Hatcher, eds. Preparing for College and University Teaching: Competencies for Graduate and Professional Students. Stylus Publishing, LLC, 2021. source
  • Müller, S. D. (2022). Student Research as Legitimate Peripheral Participation. Communications of the Association for Information Systems, 50, pp-pp. source
  • Zheng, Lily. DEI Deconstructed: Your No-nonsense Guide to Doing the Work and Doing it Right. Berrett-Koehler Publishers, 2022. source
  • Daniel T. Willingham. Outsmart Your Brain: Why Learning is Hard and How You Can Make It Easy. Gallery Books, 2023. source
  • Regan A. R. Gurung and John Dunlosky. Study Like a Champ: The Psychology-Based Guide to “Grade A” Study Habits. APA LifeTools, 2023. source
  • Barbeau, Lauren, and Claudia Cornejo Happel. "Critical Teaching Behaviors: Defining, Documenting, and Discussing Good Teaching." (2023). source
  • The website "Solve a Teaching Problem" by Eberly Center, Carnegie Mellon University provides practical strategies to address teaching problems across the disciplines.
  • Journal of Management Education
  • Management Teaching Review
  • Journal of Teaching in International Business
  • Journal of Education for Business

AI & Education⚓︎

This list comes from the Center of Teaching and Learning at the University of Texas at Dallas along with other sources.

  • Julia Staffel, ChatGPT and Its Impact on Teaching Philosophy and Other Subjects video
  • Cynthia Alby, Chatgpt: Understanding the New Landscape and Short-Term Solutions Google Docs
  • Lee Skallerup Bessette's Zotero Library on ChatGPT source
  • Teachers On Fire, Should Schools BAN ChatGPT? 4 Reasons Not To! video
  • Eric Prochaska, Embrace the Bot: Designing Writing Assignments in the Face of AI source
  • Alexandra Mihai, Let's get off the fear carousel! source
  • Art Brownlow, AI Essay Writing: Dawn in the Garden of Good and Evil video
  • Kritik Education, 12 Ways Instructors Can Use OpenAI to Transform Assessments source
  • Derek Bruff, A Bigger, Badder Clippy: Enhancing Student Learning with AI Writing Tools source
  • @herfteducator, A Teacher’s Prompt Guide to ChatGPT Aligned With 'What Works Best' pdf
  • Center for Teaching & Assessment of Learning @ University of Delaware, Considerations for Using and Addressing Advanced Automated Tools in Coursework and Assignments website
  • Gabby Jones / Bloomberg, ChatGPT Is a Wake-up Call to Revamp How We Teach Writing website
  • Joshua Wilson, Writing Without Thinking? There’s a Place for ChatGPT — If Used Properly website
  • turnitin.com, AI-generated text: What educators are saying source
  • turnitin.com, AI-generated text: An annotated hotlist for educators source
  • turnitin.com, Guide for approaching AI-generated text in your classroom source
  • SAN JOSÉ STATE UNIVERSITY, Generative AI & ChatGPT: Resources for Instructors source
  • UCLA, ChatGPT and AI Resources source
  • Lance Eaton, Classroom Policies for AI Generative Tools source

Ethics⚓︎

  • Davison, Robert M., Maris G. Martinsons, and Louie HM Wong. "The ethics of action research participation." Information Systems Journal (2021). source
  • Umphress, Elizabeth E., et al. "From the Editors: Insights on how we try to show empathy, respect, and inclusion in AMJ." Academy of Management Journal (2022). source

Dress Code⚓︎

  • Lee, Christopher. "Dressing the Professor: What to Wear for Working in Academia." Gentleman's Gazette, 8 Nov. 2018. source archive
  • Block, Marta Segal. "What to Wear on Campus." HigherEdJobs, 18 Apr. 2017. source archive
  • martinkich. "Student and Faculty Dress Codes." ACADEME BLOG, 5 Feb. 2015. source archive
  • Smart, Michael. "How a Professor Should Dress: Tips for Lecturers, Tas & Teachers." LearnPar, 21 May 202. source archive
  • Lightstone, Karen, Rob Francis, and Lucie Kocum. "University faculty style of dress and students' perception of instructor credibility." International Journal of Business and Social Science 2.15 (2011). source
  • 40+Style. "What to Wear to a Conference or Presentation to Be Stylish and Professional." 40+ Style, 3 Aug. 2020. source
  • Crestline. "What to Wear to a Conference: The Ultimate Guide." Crestline, 28 Feb. 2022. source
  • Monus, Elle. "3 Ways to Dress for a Conference." WikiHow, WikiHow, 10 Oct. 2021. source

Statistics and Probability⚓︎

  • Casella, George, and Roger L. Berger. Statistical inference. Vol. 2. Pacific Grove, CA: Duxbury, 2002. source

Optimization⚓︎

  • Boyd, Stephen, Stephen P. Boyd, and Lieven Vandenberghe. Convex optimization. Cambridge university press, 2004. pdf
  • Sundaram, Rangarajan K. A first course in optimization theory. Cambridge university press, 1996.

Bayesian Optimization⚓︎

  • Brochu, Eric, Vlad M. Cora, and Nando De Freitas. "A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning." arXiv preprint arXiv:1012.2599 (2010). source
  • Shahriari, Bobak, et al. "Taking the human out of the loop: A review of Bayesian optimization." Proceedings of the IEEE 104.1 (2015): 148-175. source
  • Frazier, Peter I. "Bayesian optimization." Recent advances in optimization and modeling of contemporary problems. Informs, 2018. 255-278. source

Microeconomic⚓︎

  • Varian, Hal R. Microeconomic analysis. WW Norton, 1992.
  • Rubinstein, Ariel. Lecture notes in microeconomic theory: the economic agent. Princeton University Press, 2012. pdf

Econometrics⚓︎

  • Greene, William H. Econometric analysis (Eight Edition). (2017).
  • Cameron, A. Colin, and Pravin K. Trivedi. Microeconometrics: methods and applications. Cambridge university press, 2005.
  • Wooldridge, Jeffrey M. Econometric analysis of cross section and panel data. MIT press, 2010.
  • Wooldridge, Jeffrey M. Introductory econometrics: A modern approach. Nelson Education, 2016.
  • Hill, R. Carter, William E. Griffiths, and Guay C. Lim. Principles of econometrics. John Wiley & Sons, 2018.
  • Davidson, Russell, and James G. MacKinnon. Econometric theory and methods. Vol. 5. New York: Oxford University Press, 2004.
  • Maddala, Gangadharrao S. Limited-dependent and qualitative variables in econometrics. No. 3. Cambridge university press, 1986.
  • Angrist, Joshua D., and Jörn-Steffen Pischke. Mostly harmless econometrics: An empiricist's companion. Princeton university press, 2008.
  • Baltagi, Badi Hani. "Econometric analysis of panel data". Springer International Publishing, (6th Edition, 2021). source
  • This Wikipedia page compares technical information for a number of statistical analysis packages.

Causal Inference⚓︎

  • Pearl, Judea. "Causal inference in statistics: An overview." Statistics surveys 3 (2009): 96-146. pdf
  • Pearl, Judea. Causality. Cambridge university press, 2009. author's website
  • Hernán, Miguel A., and James M. Robins. "Causal inference." (2010): 2. source
  • Glymour, Madelyn, Judea Pearl, and Nicholas P. Jewell. Causal inference in statistics: A primer. John Wiley & Sons, 2016. author's website
  • Peters, Jonas, Dominik Janzing, and Bernhard Schölkopf. Elements of causal inference: foundations and learning algorithms. The MIT Press, 2017. source
  • Pearl, Judea, and Dana Mackenzie. The book of why: the new science of cause and effect. Basic books, 2018. author's website
  • Yao, Liuyi, et al. "A survey on causal inference." arXiv preprint arXiv:2002.02770 (2020). source
  • Cunningham, Scott. "Causal inference." Causal Inference. Yale University Press, 2021. author's website
  • Matheus Facure's handbook Causal Inference for The Brave and True github repository
  • Brady Neal's blog provides a good list of books
  • Brady Neal's blog also provides a good course "Introduction to Causal Inference"

Game Theory⚓︎

  • Gibbons, Robert S. Game theory for applied economists. Princeton University Press, 1992.
  • Fudenberg, Drew, and Jean Tirole. Game theory. MIT press, 1991.
  • Myerson, Roger B. Game Theory: Analysis of Conflict. Harvard university press, 2013.
  • Osborne, Martin J., and Ariel Rubinstein. A course in game theory. MIT press, 1994.

Industry Organization⚓︎

  • Tirole, Jean. The theory of industrial organization. MIT press, 1988.
  • Vives, Xavier. Oligopoly pricing: old ideas and new tools. MIT press, 1999.
  • Martin, Stephen. Advanced industrial economics. Blackwell Publishers, 2002.
  • Belleflamme, Paul, and Martin Peitz. Industrial organization: markets and strategies. Cambridge University Press, 2015.

Artificial Intelligence⚓︎

  • Shmueli, Galit. "To explain or to predict?." Statistical science 25.3 (2010): 289-310. source
  • Shmueli, Galit, and Otto R. Koppius. "Predictive analytics in information systems research." MIS quarterly (2011): 553-572. source

Machine Learning⚓︎

  • Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media, 2009. source
  • Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006. pdf
  • Barber, David. Bayesian reasoning and machine learning. Cambridge University Press, 2012. source
  • Murphy, Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.
  • Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep learning." Cambridge: MIT press, 2016. source
  • Sergey Levine provides a course CS W182 / 282A - UC Berkeley at Designing, Visualizing and Understanding Deep Neural Networks
  • Murphy, Kevin P. Probabilistic machine learning: an introduction. MIT press, 2022. website
  • Murphy, Kevin P. Probabilistic machine learning: Advanced Topics. MIT press, 2023. website
  • Berente, Nicholas, et al. "Managing artificial intelligence." MIS Quarterly 45.3 (2021): 1433-1450. source
  • Balaji Padmanabhan, Xiao Fang, Nachiketa Sahoo, and Andrew Burton-Jones. "Machine Learning in Information Systems Research", MIS Quarterly Editors' Comments, 2022. source

Few-shot Learning⚓︎

  • Wang, Yaqing, et al. "Generalizing from a few examples: A survey on few-shot learning." ACM Computing Surveys (CSUR) 53.3 (2020): 1-34. source

Transfer Learning⚓︎

  • Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359. source
  • Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. "A survey of transfer learning." Journal of Big data 3.1 (2016): 1-40. source
  • Tan, Chuanqi, et al. "A survey on deep transfer learning." International conference on artificial neural networks. Springer, Cham, 2018. source
  • Zhuang, Fuzhen, et al. "A comprehensive survey on transfer learning." Proceedings of the IEEE 109.1 (2020): 43-76. source

Reinforcement Learning⚓︎

Computer Vision⚓︎

Natural Language Processing⚓︎

  • keon on github.com provides a curated list dedicated to natural language processing at awesome-nlp

Stochastic Differential Equation⚓︎

  • Gardiner, Crispin W. "Handbook of stochastic methods: for physics, chemistry and the natural sciences." (2004). source
  • Dixit, Robert K., and Robert S. Pindyck. "Investment under uncertainty". Princeton university press, 2012. source
  • Klebaner, Fima C. "Introduction to stochastic calculus with applications". World Scientific Publishing Company, 2012. source
  • Evans, Lawrence C. "An introduction to stochastic differential equations". Vol. 82. American Mathematical Soc., 2012. pdf
  • Mikosch, Thomas. "Elementary stochastic calculus with finance in view". World scientific, 1998. source
  • Oksendal, Bernt. "Stochastic differential equations: an introduction with applications". Springer Science & Business Media, 2013. source
  • Mao, Xuerong. "Stochastic differential equations and applications". Elsevier, 2007. source

Theory Building⚓︎

  • Hassan, Nik Rushdi; Lowry, Paul Benjamin; and Mathiassen, Lars (2022) "Useful Products in Information Systems Theorizing: A Discursive Formation Perspective," Journal of the Association for Information Systems, 23(2), 418-446. source

Code Analysis⚓︎

© License⚓︎

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Last Update On 2023-06-02.

Copyright © 2019 - 2023 Yihong Liu
\ No newline at end of file diff --git a/research/analytical/index.html b/research/analytical/index.html index 502c303..664ba88 100644 --- a/research/analytical/index.html +++ b/research/analytical/index.html @@ -1 +1 @@ - Analytical Model - MIS Reading List

Analytical Model⚓︎

How to Model⚓︎

  • Varian, Hal R. "How to build an economic model in your spare time." The American Economist 61.1 (2016): 81-90. pdf
  • Tobias Crönert, Stefan Minner (2022) Equilibrium Identification and Selection in Finite Games. Operations Research 0(0). source

Bounded Rationality and Attention⚓︎

  • Gifford, Sharon. "Limited attention as the bound on rationality." The BE Journal of Theoretical Economics 5.1 (2005). source

Adverse Selection and Self Selection⚓︎

  • Akerlof, George A. "The market for “lemons”: Quality uncertainty and the market mechanism." Uncertainty in economics. Academic Press, 1978. 235-251. source
  • Sundararajan, Arun. "Nonlinear pricing of information goods." Management science 50.12 (2004): 1660-1673. source
  • Samir Mamadehussene (2023) Rebates Offered by a Multiproduct Firm. Marketing Science 0(0). https://doi.org/10.1287/mksc.2023.1430

Two-sided Market⚓︎

  • Tunc, Murat M., Huseyin Cavusoglu, and Srinivasan Raghunathan. "Two-Sided Adverse Selection and Bilateral Reviews in Sharing Economy." Available at SSRN 3499979 (2019). source
  • Yifan Dou, D. J. Wu (2021) Platform Competition Under Network Effects: Piggybacking and Optimal Subsidization. Information Systems Research 32(3):820-835. source
  • Manlu Chen, Ming Hu, Jianfu Wang (2022) Food Delivery Service and Restaurant: Friend or Foe?. Management Science 0(0). source
  • Saeed Alaei, Ali Makhdoumi, Azarakhsh Malekian, Saša Pekeč (2022) Revenue-Sharing Allocation Strategies for Two-Sided Media Platforms: Pro-Rata vs. User-Centric. Management Science 0(0). source
  • Haurand, M. D. (2022). Looking Beyond Membership: A Simulation Study of Market-entry Strategies for Two-sided Platforms under Competition. Communications of the Association for Information Systems, 50, pp-pp. source
  • Elias Carroni, Leonardo Madio, Shiva Shekhar (2023) Superstar Exclusivity in Two-Sided Markets. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4720

Externalities⚓︎

  • August, Terrence, and Tunay I. Tunca. "Network software security and user incentives." Management Science 52.11 (2006): 1703-1720. source
  • Koh, Byungwan, Srinivasan Raghunathan, and Barrie R. Nault. "Is voluntary profiling welfare enhancing?." Management Information Systems Quarterly, Forthcoming (2015). source

Platform⚓︎

Online Platform⚓︎

  • Alexandre de Cornière, Miklos Sarvary (2022) Social Media and News: Content Bundling and News Quality. Management Science 0(0). source
  • Yunke Mai, Bin Hu, Saša Pekeč (2022) Courteous or Crude? Managing User Conduct to Improve On-Demand Service Platform Performance. Management Science 0(0). source
  • Pnina Feldman, Andrew E. Frazelle, Robert Swinney (2022) Managing Relationships Between Restaurants and Food Delivery Platforms: Conflict, Contracts, and Coordination. Management Science 0(0). source

User Generated Content⚓︎

  • Dongwook Shin, Stefano Vaccari, Assaf Zeevi (2022) Dynamic Pricing with Online Reviews. Management Science 0(0). source
  • Pu, Jingchuan, et al. "Platform policies and sellers’ competition in agency selling in the presence of online quality misrepresentation." Journal of Management Information Systems 39.1 (2022): 159-186. source
  • Shin, Dongwook, and Assaf Zeevi. "Product quality and information sharing in the presence of reviews." Management Science (2023). https://doi.org/10.1287/mnsc.2023.4746

Platform Openness⚓︎

  • Adner, Ron, Jianqing Chen, and Feng Zhu. "Frenemies in platform markets: Heterogeneous profit foci as drivers of compatibility decisions." Management Science (2019). source
  • Chen, Jianqing, and Zhiling Guo. "New media advertising and retail platform openness." source

Versioning⚓︎

  • Bhargava, Hemant K., and Vidyanand Choudhary. "Information goods and vertical differentiation." Journal of Management Information Systems 18.2 (2001): 89-106. source
  • Lahiri, Atanu, and Debabrata Dey. "Versioning and information dissemination: A new perspective." Information Systems Research 29.4 (2018): 965-983. source

Contracting and Moral Hazard⚓︎

  • Cezar, Asunur, Huseyin Cavusoglu, and Srinivasan Raghunathan. "Outsourcing information security: Contracting issues and security implications." Management Science 60.3 (2014): 638-657. source
  • Choudhary, Vidyanand, et al. "Personalized pricing and quality differentiation." Management Science 51.7 (2005): 1120-1130. source
  • Jiri Chod, Nikolaos Trichakis, S. Alex Yang (2022) Platform Tokenization: Financing, Governance, and Moral Hazard. Management Science 0(0). source
  • Huseyin Gurkan, Francis de Véricourt (2022) Contracting, Pricing, and Data Collection Under the AI Flywheel Effect. Management Science 0(0). source

Security⚓︎

  • Dey, Debabrata, Atanu Lahiri, and Guoying Zhang. "Hacker behavior, network effects, and the security software market." Journal of Management Information Systems 29.2 (2012): 77-108. source
  • Ghoshal, Abhijeet, Atanu Lahiri, and Debabrata Dey. "Drawing a Line in the Sand: Commitment Problem in Ending Software Support." MIS Quarterly 41.4 (2017): 1227-1247. source
  • Terrence August, Duy Dao, Marius Florin Niculescu (2022) Economics of Ransomware: Risk Interdependence and Large-Scale Attacks. Management Science 0(0). source

Privacy⚓︎

  • T. Tony Ke, K. Sudhir (2022) Privacy Rights and Data Security: GDPR and Personal Data Markets. Management Science 0(0). source
  • Ashkan Eshghi, Ram D. Gopal, Hooman Hidaji, Raymond A. Patterson (2023) Now You See It, Now You Don’t: Obfuscation of Online Third-Party Information Sharing. INFORMS Journal on Computing 0(0). https://doi.org/10.1287/ijoc.2022.1266

Piracy⚓︎

  • Lahiri, Atanu, and Debabrata Dey. "Effects of piracy on quality of information goods." Management Science 59.1 (2013): 245-264. source
  • Kim, Antino, Atanu Lahiri, and Debabrata Dey. "The" Invisible Hand" of Piracy: An Economic Analysis of the Information-Goods Supply Chain." MIS Quarterly 42.4 (2018). source
  • Chellappa, Ramnath K., and Shivendu Shivendu. "Managing piracy: Pricing and sampling strategies for digital experience goods in vertically segmented markets." Information Systems Research 16.4 (2005): 400-417. source
  • Jain, Sanjay. "Digital piracy: A competitive analysis." Marketing science 27.4 (2008): 610-626. source
  • Peitz, Martin, and Patrick Waelbroeck. "Piracy of digital products: A critical review of the theoretical literature." Information Economics and Policy 18.4 (2006): 449-476. source
  • Jin, Chen, Chenguang Wu, and Atanu Lahiri. "Piracy and Bundling of Information Goods." Journal of Management Information Systems 39.3 (2022): 906-933. source
  • Can Sun, Yonghua Ji, Xianjun Geng (2023) Which Enemy to Dance with? A New Role of Software Piracy in Influencing Antipiracy Strategies. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1219

Online Advertising⚓︎

  • Chen, Jianqing, and Jan Stallaert. "An economic analysis of online advertising using behavioral targeting." Mis Quarterly 38.2 (2014): 429-A7. source
  • Jiwoong Shin, Woochoel Shin (2022) A Theory of Irrelevant Advertising: An Agency-Induced Targeting Inefficiency. Management Science 0(0). source
  • Stylianos Despotakis, Jungju Yu (2022) Multidimensional Targeting and Consumer Response. Management Science 0(0). source
  • Sridhar Moorthy, Shervin Shahrokhi Tehrani (2023) Targeting Advertising Spending and Price on the Hotelling Line. Marketing Science 0(0). source

Auction⚓︎

  • Liu, De, Jianqing Chen, and Andrew B. Whinston. "Ex ante information and the design of keyword auctions." Information Systems Research 21.1 (2010): 133-153.source
  • Vincent Conitzer, Christian Kroer, Debmalya Panigrahi, Okke Schrijvers, Nicolas E. Stier-Moses, Eric Sodomka, Christopher A. Wilkens (2022) Pacing Equilibrium in First Price Auction Markets. Management Science 0(0). source
  • Thomas Nedelec, Clément Calauzènes, Vianney Perchet, Noureddine El Karoui (2022) Revenue-Maximizing Auctions: A Bidder’s Standpoint. Operations Research 0(0). source
  • Santiago Balseiro, Christian Kroer, Rachitesh Kumar (2023) Contextual Standard Auctions with Budgets: Revenue Equivalence and Efficiency Guarantees. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4719

Recommendation & Personalization⚓︎

  • Ghoshal, Abhijeet, Vijay S. Mookerjee, and Sumit Sarkar. "Recommendations and Cross-selling: Pricing Strategies when Personalizing Firms Cross-sell." Journal of Management Information Systems 38.2 (2021): 430-456. source
  • Didier Laussel, Joana Resende (2022) When Is Product Personalization Profit-Enhancing? A Behavior-Based Discrimination Model. Management Science 0(0). source
  • Odilon Câmara, Nan Jia, Joseph Raffiee (2023) Reputation, Competition, and Lies in Labor Market Recommendations. Management Science 0(0). source
  • Cao, H. Henry, et al. "How does competition affect exploration vs. exploitation? a tale of two recommendation algorithms." Management Science (2023). https://doi.org/10.1287/mnsc.2023.4722

Price Discrimination⚓︎

  • Xi Li, Zibin Xu (2022) Superior Knowledge, Price Discrimination, and Customer Inspection. Marketing Science 0(0). source
  • Amir Ajorlou, Ali Jadbabaie (2023) Sales-Based Rebate Design. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4691
  • Chongwoo Choe, Jiajia Cong, Chengsi Wang (2023) Softening Competition Through Unilateral Sharing of Customer Data. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4689

Competition⚓︎

Price Competition⚓︎

  • Junhyun Bae, Li Chen, Shiqing Yao (2022) Service Capacity and Price Promotion Wars. Management Science 0(0). source

Information Revelation⚓︎

  • Ganesh Iyer, Shubhranshu Singh (2022) Persuasion Contest: Disclosing Own and Rival Information. Marketing Science 0(0). source

Short-Termism⚓︎

  • Xiaoyan Liu, William Schmidt (2022) Operational Distortion: Compound Effects of Short-Termism and Competition. Management Science 0(0). source

Market⚓︎

  • Jun Pei, Ping Yan, Subodha Kumar (2023) No Permanent Friend or Enemy: Impacts of the IIoT-Based Platform in the Maintenance Service Market. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4733

Collaboration⚓︎

  • Shubham Gupta, Abhishek Roy, Subodha Kumar, Ram Mudambi (2022) When Worse Is Better: Strategic Choice of Vendors with Differentiated Capabilities in a Complex Cocreation Environment. Management Science 0(0). source

Retailers Strategy⚓︎

  • Honggang Hu, Quan Zheng, Xiajun Amy Pan (2022) Agency or Wholesale? The Role of Retail Pass-Through. Management Science 0(0). source
  • Yu An, Zeyu Zheng (2022) Immediacy Provision and Matchmaking. Management Science 0(0). source
  • Yuefeng Li, Moutaz J. Khouja, Jingming Pan, Jing Zhou (2022) Buy-One-Get-One Promotions in a Two-Echelon Supply Chain. Management Science 0(0). source

Innovation⚓︎

  • Byungyeon Kim, Oded Koenigsberg, Elie Ofek (2022) I Don’t “Recall”: The Decision to Delay Innovation Launch to Avoid Costly Product Failure. Management Science 0(0). source

Historical Price⚓︎

  • Zheng Gong, Jin Huang, Yuxin Chen (2022) What the Past Tells About the Future: Historical Prices in the Durable Goods Market. Management Science 0(0). source

Sustainability⚓︎

  • Xiaoshuai Fan, Kanglin Chen, Ying-Ju Chen (2022) Is Price Commitment a Better Solution to Control Carbon Emissions and Promote Technology Investment?. Management Science 0(0). source
  • Chen Jin, Luyi Yang, Cungen Zhu (2022) Right to Repair: Pricing, Welfare, and Environmental Implications. Management Science 0(0). source

Stochastic Game Theory⚓︎

  • Bar Light, Gabriel Y. Weintraub (2021) Mean Field Equilibrium: Uniqueness, Existence, and Comparative Statics. Operations Research 70(1):585-605. source

Customization⚓︎

  • Gökçe Esenduran, Paolo Letizia, Anton Ovchinnikov (2022) Customization and Returns. Management Science 0(0). source

Network⚓︎

-Mohamed Mostagir, James Siderius (2022) Social Inequality and the Spread of Misinformation. Management Science 0(0). source

Information Sharing⚓︎

  • Sanjith Gopalakrishnan, Moksh Matta, Hasan Cavusoglu (2022) The Dark Side of Technological Modularity: Opportunistic Information Hiding During Interorganizational System Adoption. Information Systems Research 0(0). source

Information Nudges⚓︎

  • Xiao, Ping, et al. "The Effects of Information Nudges on Consumer Usage of Digital Services under Three-Part Tariffs." Journal of Management Information Systems 39.1 (2022): 130-158. source

Repeated Purchase⚓︎

  • Aslan Lotfi, Zhengrui Jiang, Ali Lotfi, Dipak C. Jain (2022) Estimating Life Cycle Sales of Technology Products with Frequent Repeat Purchases: A Fractional Calculus-Based Approach. Information Systems Research 0(0). source

Health⚓︎

  • Wilfred Amaldoss, Mushegh Harutyunyan (2022) Pricing of Vice Goods for Goal-Driven Consumers. Management Science 0(0). source
  • Nan Liu, Willem van Jaarsveld, Shan Wang, Guanlian Xiao (2023) Managing Outpatient Service with Strategic Walk-ins. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4676

Data Market⚓︎

  • Kimon Drakopoulos, Ali Makhdoumi (2022) Providing Data Samples for Free. Management Science 0(0). source

Blockchain⚓︎

  • Garud Iyengar, Fahad Saleh, Jay Sethuraman, Wenjun Wang (2022) Economics of Permissioned Blockchain Adoption. Management Science 0(0). source
  • Benedikt Franke, Qi Gao Fritz, André Stenzel (2023) The (Limited) Power of Blockchain Networks for Information Provision. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4718
  • Basu, Soumya, et al. "StableFees: A predictable fee market for cryptocurrencies." Management Science (2023). https://doi.org/10.1287/mnsc.2023.4735
  • Michael Sockin, Wei Xiong (2023) A Model of Cryptocurrencies. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4756

Counterfeits⚓︎

  • Yuetao Gao, Yue Wu (2023) Regulating Probabilistic Selling of Counterfeits. Management Science 0(0). https://doi.orglibproxy.utdallas.edu/10.1287/mnsc.2022.4607

Reputation⚓︎

  • Xiang Hui, Zekun Liu, Weiqing Zhang (2023) From High Bar to Uneven Bars: The Impact of Information Granularity in Quality Certification. Management Science 0(0). https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2022.4666

Dynamic Pricing⚓︎

  • Daniel Garcia, Maarten C. W. Janssen, Radostina Shopova (2023) Dynamic Pricing with Uncertain Capacities. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4613

Subscription⚓︎

  • W. Jason Choi, Qihong Liu, Jiwoong Shin (2023) Predictive Analytics and Ship-Then-Shop Subscription. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4723

Preference and Choice⚓︎

  • Junnan He (2023) Bayesian Contextual Choices Under Imperfect Perception of Attributes. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4751

Ride Sharing⚓︎

  • Qi (George) Chen, Yanzhe (Murray) Lei, Stefanus Jasin (2023) Real-Time Spatial–Intertemporal Pricing and Relocation in a Ride-Hailing Network: Near-Optimal Policies and the Value of Dynamic Pricing. Operations Research 0(0). https://doi.org/10.1287/opre.2022.2425

© License⚓︎

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Last Update On 2023-04-15.

Copyright © 2019 - 2023 Yihong Liu
\ No newline at end of file + Analytical Model - MIS Reading List

Analytical Model⚓︎

How to Model⚓︎

  • Varian, Hal R. "How to build an economic model in your spare time." The American Economist 61.1 (2016): 81-90. pdf
  • Tobias Crönert, Stefan Minner (2022) Equilibrium Identification and Selection in Finite Games. Operations Research 0(0). source

Bounded Rationality and Attention⚓︎

  • Gifford, Sharon. "Limited attention as the bound on rationality." The BE Journal of Theoretical Economics 5.1 (2005). source

Adverse Selection and Self Selection⚓︎

  • Akerlof, George A. "The market for “lemons”: Quality uncertainty and the market mechanism." Uncertainty in economics. Academic Press, 1978. 235-251. source
  • Sundararajan, Arun. "Nonlinear pricing of information goods." Management science 50.12 (2004): 1660-1673. source
  • Samir Mamadehussene (2023) Rebates Offered by a Multiproduct Firm. Marketing Science 0(0). https://doi.org/10.1287/mksc.2023.1430

Two-sided Market⚓︎

  • Tunc, Murat M., Huseyin Cavusoglu, and Srinivasan Raghunathan. "Two-Sided Adverse Selection and Bilateral Reviews in Sharing Economy." Available at SSRN 3499979 (2019). source
  • Yifan Dou, D. J. Wu (2021) Platform Competition Under Network Effects: Piggybacking and Optimal Subsidization. Information Systems Research 32(3):820-835. source
  • Manlu Chen, Ming Hu, Jianfu Wang (2022) Food Delivery Service and Restaurant: Friend or Foe?. Management Science 0(0). source
  • Saeed Alaei, Ali Makhdoumi, Azarakhsh Malekian, Saša Pekeč (2022) Revenue-Sharing Allocation Strategies for Two-Sided Media Platforms: Pro-Rata vs. User-Centric. Management Science 0(0). source
  • Haurand, M. D. (2022). Looking Beyond Membership: A Simulation Study of Market-entry Strategies for Two-sided Platforms under Competition. Communications of the Association for Information Systems, 50, pp-pp. source
  • Elias Carroni, Leonardo Madio, Shiva Shekhar (2023) Superstar Exclusivity in Two-Sided Markets. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4720

Externalities⚓︎

  • August, Terrence, and Tunay I. Tunca. "Network software security and user incentives." Management Science 52.11 (2006): 1703-1720. source
  • Koh, Byungwan, Srinivasan Raghunathan, and Barrie R. Nault. "Is voluntary profiling welfare enhancing?." Management Information Systems Quarterly, Forthcoming (2015). source

Platform⚓︎

Online Platform⚓︎

  • Alexandre de Cornière, Miklos Sarvary (2022) Social Media and News: Content Bundling and News Quality. Management Science 0(0). source
  • Yunke Mai, Bin Hu, Saša Pekeč (2022) Courteous or Crude? Managing User Conduct to Improve On-Demand Service Platform Performance. Management Science 0(0). source
  • Pnina Feldman, Andrew E. Frazelle, Robert Swinney (2022) Managing Relationships Between Restaurants and Food Delivery Platforms: Conflict, Contracts, and Coordination. Management Science 0(0). source

User Generated Content⚓︎

  • Dongwook Shin, Stefano Vaccari, Assaf Zeevi (2022) Dynamic Pricing with Online Reviews. Management Science 0(0). source
  • Pu, Jingchuan, et al. "Platform policies and sellers’ competition in agency selling in the presence of online quality misrepresentation." Journal of Management Information Systems 39.1 (2022): 159-186. source
  • Shin, Dongwook, and Assaf Zeevi. "Product quality and information sharing in the presence of reviews." Management Science (2023). https://doi.org/10.1287/mnsc.2023.4746

Platform Openness⚓︎

  • Adner, Ron, Jianqing Chen, and Feng Zhu. "Frenemies in platform markets: Heterogeneous profit foci as drivers of compatibility decisions." Management Science (2019). source
  • Chen, Jianqing, and Zhiling Guo. "New media advertising and retail platform openness." source

Versioning⚓︎

  • Bhargava, Hemant K., and Vidyanand Choudhary. "Information goods and vertical differentiation." Journal of Management Information Systems 18.2 (2001): 89-106. source
  • Lahiri, Atanu, and Debabrata Dey. "Versioning and information dissemination: A new perspective." Information Systems Research 29.4 (2018): 965-983. source

Contracting and Moral Hazard⚓︎

  • Cezar, Asunur, Huseyin Cavusoglu, and Srinivasan Raghunathan. "Outsourcing information security: Contracting issues and security implications." Management Science 60.3 (2014): 638-657. source
  • Choudhary, Vidyanand, et al. "Personalized pricing and quality differentiation." Management Science 51.7 (2005): 1120-1130. source
  • Jiri Chod, Nikolaos Trichakis, S. Alex Yang (2022) Platform Tokenization: Financing, Governance, and Moral Hazard. Management Science 0(0). source
  • Huseyin Gurkan, Francis de Véricourt (2022) Contracting, Pricing, and Data Collection Under the AI Flywheel Effect. Management Science 0(0). source

Security⚓︎

  • Dey, Debabrata, Atanu Lahiri, and Guoying Zhang. "Hacker behavior, network effects, and the security software market." Journal of Management Information Systems 29.2 (2012): 77-108. source
  • Ghoshal, Abhijeet, Atanu Lahiri, and Debabrata Dey. "Drawing a Line in the Sand: Commitment Problem in Ending Software Support." MIS Quarterly 41.4 (2017): 1227-1247. source
  • Terrence August, Duy Dao, Marius Florin Niculescu (2022) Economics of Ransomware: Risk Interdependence and Large-Scale Attacks. Management Science 0(0). source

Privacy⚓︎

  • T. Tony Ke, K. Sudhir (2022) Privacy Rights and Data Security: GDPR and Personal Data Markets. Management Science 0(0). source
  • Ashkan Eshghi, Ram D. Gopal, Hooman Hidaji, Raymond A. Patterson (2023) Now You See It, Now You Don’t: Obfuscation of Online Third-Party Information Sharing. INFORMS Journal on Computing 0(0). https://doi.org/10.1287/ijoc.2022.1266

Piracy⚓︎

  • Lahiri, Atanu, and Debabrata Dey. "Effects of piracy on quality of information goods." Management Science 59.1 (2013): 245-264. source
  • Kim, Antino, Atanu Lahiri, and Debabrata Dey. "The" Invisible Hand" of Piracy: An Economic Analysis of the Information-Goods Supply Chain." MIS Quarterly 42.4 (2018). source
  • Chellappa, Ramnath K., and Shivendu Shivendu. "Managing piracy: Pricing and sampling strategies for digital experience goods in vertically segmented markets." Information Systems Research 16.4 (2005): 400-417. source
  • Jain, Sanjay. "Digital piracy: A competitive analysis." Marketing science 27.4 (2008): 610-626. source
  • Peitz, Martin, and Patrick Waelbroeck. "Piracy of digital products: A critical review of the theoretical literature." Information Economics and Policy 18.4 (2006): 449-476. source
  • Jin, Chen, Chenguang Wu, and Atanu Lahiri. "Piracy and Bundling of Information Goods." Journal of Management Information Systems 39.3 (2022): 906-933. source
  • Can Sun, Yonghua Ji, Xianjun Geng (2023) Which Enemy to Dance with? A New Role of Software Piracy in Influencing Antipiracy Strategies. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1219

Online Advertising⚓︎

  • Chen, Jianqing, and Jan Stallaert. "An economic analysis of online advertising using behavioral targeting." Mis Quarterly 38.2 (2014): 429-A7. source
  • Jiwoong Shin, Woochoel Shin (2022) A Theory of Irrelevant Advertising: An Agency-Induced Targeting Inefficiency. Management Science 0(0). source
  • Stylianos Despotakis, Jungju Yu (2022) Multidimensional Targeting and Consumer Response. Management Science 0(0). source
  • Sridhar Moorthy, Shervin Shahrokhi Tehrani (2023) Targeting Advertising Spending and Price on the Hotelling Line. Marketing Science 0(0). source

Auction⚓︎

  • Liu, De, Jianqing Chen, and Andrew B. Whinston. "Ex ante information and the design of keyword auctions." Information Systems Research 21.1 (2010): 133-153.source
  • Vincent Conitzer, Christian Kroer, Debmalya Panigrahi, Okke Schrijvers, Nicolas E. Stier-Moses, Eric Sodomka, Christopher A. Wilkens (2022) Pacing Equilibrium in First Price Auction Markets. Management Science 0(0). source
  • Thomas Nedelec, Clément Calauzènes, Vianney Perchet, Noureddine El Karoui (2022) Revenue-Maximizing Auctions: A Bidder’s Standpoint. Operations Research 0(0). source
  • Santiago Balseiro, Christian Kroer, Rachitesh Kumar (2023) Contextual Standard Auctions with Budgets: Revenue Equivalence and Efficiency Guarantees. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4719

Recommendation & Personalization⚓︎

  • Ghoshal, Abhijeet, Vijay S. Mookerjee, and Sumit Sarkar. "Recommendations and Cross-selling: Pricing Strategies when Personalizing Firms Cross-sell." Journal of Management Information Systems 38.2 (2021): 430-456. source
  • Didier Laussel, Joana Resende (2022) When Is Product Personalization Profit-Enhancing? A Behavior-Based Discrimination Model. Management Science 0(0). source
  • Odilon Câmara, Nan Jia, Joseph Raffiee (2023) Reputation, Competition, and Lies in Labor Market Recommendations. Management Science 0(0). source
  • Cao, H. Henry, et al. "How does competition affect exploration vs. exploitation? a tale of two recommendation algorithms." Management Science (2023). https://doi.org/10.1287/mnsc.2023.4722

Price Discrimination⚓︎

  • Xi Li, Zibin Xu (2022) Superior Knowledge, Price Discrimination, and Customer Inspection. Marketing Science 0(0). source
  • Amir Ajorlou, Ali Jadbabaie (2023) Sales-Based Rebate Design. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4691
  • Chongwoo Choe, Jiajia Cong, Chengsi Wang (2023) Softening Competition Through Unilateral Sharing of Customer Data. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4689

Competition⚓︎

Price Competition⚓︎

  • Junhyun Bae, Li Chen, Shiqing Yao (2022) Service Capacity and Price Promotion Wars. Management Science 0(0). source

Information Revelation⚓︎

  • Ganesh Iyer, Shubhranshu Singh (2022) Persuasion Contest: Disclosing Own and Rival Information. Marketing Science 0(0). source

Short-Termism⚓︎

  • Xiaoyan Liu, William Schmidt (2022) Operational Distortion: Compound Effects of Short-Termism and Competition. Management Science 0(0). source

Market⚓︎

  • Jun Pei, Ping Yan, Subodha Kumar (2023) No Permanent Friend or Enemy: Impacts of the IIoT-Based Platform in the Maintenance Service Market. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4733

Collaboration⚓︎

  • Shubham Gupta, Abhishek Roy, Subodha Kumar, Ram Mudambi (2022) When Worse Is Better: Strategic Choice of Vendors with Differentiated Capabilities in a Complex Cocreation Environment. Management Science 0(0). source

Retailers Strategy⚓︎

  • Honggang Hu, Quan Zheng, Xiajun Amy Pan (2022) Agency or Wholesale? The Role of Retail Pass-Through. Management Science 0(0). source
  • Yu An, Zeyu Zheng (2022) Immediacy Provision and Matchmaking. Management Science 0(0). source
  • Yuefeng Li, Moutaz J. Khouja, Jingming Pan, Jing Zhou (2022) Buy-One-Get-One Promotions in a Two-Echelon Supply Chain. Management Science 0(0). source

Innovation⚓︎

  • Byungyeon Kim, Oded Koenigsberg, Elie Ofek (2022) I Don’t “Recall”: The Decision to Delay Innovation Launch to Avoid Costly Product Failure. Management Science 0(0). source

Historical Price⚓︎

  • Zheng Gong, Jin Huang, Yuxin Chen (2022) What the Past Tells About the Future: Historical Prices in the Durable Goods Market. Management Science 0(0). source

Sustainability⚓︎

  • Xiaoshuai Fan, Kanglin Chen, Ying-Ju Chen (2022) Is Price Commitment a Better Solution to Control Carbon Emissions and Promote Technology Investment?. Management Science 0(0). source
  • Chen Jin, Luyi Yang, Cungen Zhu (2022) Right to Repair: Pricing, Welfare, and Environmental Implications. Management Science 0(0). source

Stochastic Game Theory⚓︎

  • Bar Light, Gabriel Y. Weintraub (2021) Mean Field Equilibrium: Uniqueness, Existence, and Comparative Statics. Operations Research 70(1):585-605. source

Customization⚓︎

  • Gökçe Esenduran, Paolo Letizia, Anton Ovchinnikov (2022) Customization and Returns. Management Science 0(0). source

Network⚓︎

-Mohamed Mostagir, James Siderius (2022) Social Inequality and the Spread of Misinformation. Management Science 0(0). source

Information Sharing⚓︎

  • Sanjith Gopalakrishnan, Moksh Matta, Hasan Cavusoglu (2022) The Dark Side of Technological Modularity: Opportunistic Information Hiding During Interorganizational System Adoption. Information Systems Research 0(0). source

Information Nudges⚓︎

  • Xiao, Ping, et al. "The Effects of Information Nudges on Consumer Usage of Digital Services under Three-Part Tariffs." Journal of Management Information Systems 39.1 (2022): 130-158. source

Repeated Purchase⚓︎

  • Aslan Lotfi, Zhengrui Jiang, Ali Lotfi, Dipak C. Jain (2022) Estimating Life Cycle Sales of Technology Products with Frequent Repeat Purchases: A Fractional Calculus-Based Approach. Information Systems Research 0(0). source

Health⚓︎

  • Wilfred Amaldoss, Mushegh Harutyunyan (2022) Pricing of Vice Goods for Goal-Driven Consumers. Management Science 0(0). source
  • Nan Liu, Willem van Jaarsveld, Shan Wang, Guanlian Xiao (2023) Managing Outpatient Service with Strategic Walk-ins. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4676

Data Market⚓︎

  • Kimon Drakopoulos, Ali Makhdoumi (2022) Providing Data Samples for Free. Management Science 0(0). source

Blockchain⚓︎

  • Garud Iyengar, Fahad Saleh, Jay Sethuraman, Wenjun Wang (2022) Economics of Permissioned Blockchain Adoption. Management Science 0(0). source
  • Benedikt Franke, Qi Gao Fritz, André Stenzel (2023) The (Limited) Power of Blockchain Networks for Information Provision. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4718
  • Basu, Soumya, et al. "StableFees: A predictable fee market for cryptocurrencies." Management Science (2023). https://doi.org/10.1287/mnsc.2023.4735
  • Michael Sockin, Wei Xiong (2023) A Model of Cryptocurrencies. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4756

Counterfeits⚓︎

  • Yuetao Gao, Yue Wu (2023) Regulating Probabilistic Selling of Counterfeits. Management Science 0(0). https://doi.orglibproxy.utdallas.edu/10.1287/mnsc.2022.4607

Reputation⚓︎

  • Xiang Hui, Zekun Liu, Weiqing Zhang (2023) From High Bar to Uneven Bars: The Impact of Information Granularity in Quality Certification. Management Science 0(0). https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2022.4666

Dynamic Pricing⚓︎

  • Daniel Garcia, Maarten C. W. Janssen, Radostina Shopova (2023) Dynamic Pricing with Uncertain Capacities. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4613

Subscription⚓︎

  • W. Jason Choi, Qihong Liu, Jiwoong Shin (2023) Predictive Analytics and Ship-Then-Shop Subscription. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4723

Preference and Choice⚓︎

  • Junnan He (2023) Bayesian Contextual Choices Under Imperfect Perception of Attributes. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4751

Ride Sharing⚓︎

  • Qi (George) Chen, Yanzhe (Murray) Lei, Stefanus Jasin (2023) Real-Time Spatial–Intertemporal Pricing and Relocation in a Ride-Hailing Network: Near-Optimal Policies and the Value of Dynamic Pricing. Operations Research 0(0). https://doi.org/10.1287/opre.2022.2425

© License⚓︎

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Last Update On 2023-04-15.

Copyright © 2019 - 2023 Yihong Liu
\ No newline at end of file diff --git a/research/empirical/index.html b/research/empirical/index.html index 9c58ec8..f81c760 100644 --- a/research/empirical/index.html +++ b/research/empirical/index.html @@ -1 +1 @@ - Empirical Model - MIS Reading List

Empirical Model⚓︎

Empirical Methodology⚓︎

  • Bertrand, Marianne, Esther Duflo, and Sendhil Mullainathan. "How much should we trust differences-in-differences estimates?." The Quarterly journal of economics 119.1 (2004): 249-275. source
  • Tafti, Ali R., and Galit Shmueli. "Beyond Overall Treatment Effects: Leveraging Covariates in Randomized Experiments Guided by Causal Structure." Available at SSRN 3331772 (2019). source
  • Xu, Yiqing. "Generalized synthetic control method: Causal inference with interactive fixed effects models." Political Analysis 25.1 (2017): 57-76.source
  • Rubin, Donald B., and Richard P. Waterman. "Estimating the causal effects of marketing interventions using propensity score methodology." Statistical Science (2006): 206-222. source
  • Athey, Susan, and Stefan Wager. "Estimating treatment effects with causal forests: An application." arXiv preprint arXiv:1902.07409 (2019). source
  • Langer, Nishtha, Ram D. Gopal, and Ravi Bapna. "Onward and Upward? An Empirical Investigation of Gender and Promotions in Information Technology Services." Information Systems Research (2020). source
  • Zhang, Yingjie, et al. "Personalized mobile targeting with user engagement stages: Combining a structural hidden markov model and field experiment." Information Systems Research 30.3 (2019): 787-804. source
  • Zhong, Ning, and David A. Schweidel. "Capturing changes in social media content: a multiple latent changepoint topic model." Marketing Science (2020). source
  • Bertsimas, Dimitris, and Nathan Kallus. "From predictive to prescriptive analytics." Management Science 66.3 (2020): 1025-1044. source
  • Wang, Guihua, Jun Li, and Wallace J. Hopp. "An instrumental variable tree approach for detecting heterogeneous treatment effects in observational studies." Ross School of Business Paper (2018). source
  • Jing Peng (2022) Identification of Causal Mechanisms from Randomized Experiments: A Framework for Endogenous Mediation Analysis. Information Systems Research 0(0). source
  • Jiaxu Peng, Jungpil Hahn, Ke-Wei Huang (2022) Handling Missing Values in Information Systems Research: A Review of Methods and Assumptions. Information Systems Research 0(0). source
  • Goldfarb A, Tucker C, Wang Y. Conducting Research in Marketing with Quasi-Experiments. Journal of Marketing. 2022;86(3):1-20. source
  • Mattke, J., Maier, C., Weitzel, T., Gerow, J. E., & Thatcher, J. B. (2022). Qualitative Comparative Analysis (QCA) In Information Systems Research: Status Quo, Guidelines, and Future Directions. Communications of the Association for Information Systems, 50, pp-pp. source
  • Haschka, Rouven E. “Handling Endogenous Regressors Using Copulas: A Generalization to Linear Panel Models with Fixed Effects and Correlated Regressors.” Journal of Marketing Research, Apr. 2022. source
  • Skinner, Richard J.; Nelson, R. Ryan; and Chin, Wynne (2022) "Synthesizing Qualitative Evidence: A Roadmap for Information Systems Research," Journal of the Association for Information Systems, 23(3), 639-677. source
  • Jiang, Dan; Jiang, Lianlian (Dorothy); Jackie, Jackie Jr.; Grover, Varun; and Sun, Heshan. 2022. "Everything Old Can Be New Again: Reinvigorating Theory Borrowing for the Digital Age," MIS Quarterly, (46: 4) pp.1833-1850. source
  • Golder, Peter N., et al. "Learning from data: An empirics-first approach to relevant knowledge generation." Journal of Marketing (2022). source
  • Fink, Lior (2022) "Why and How Online Experiments Can Benefit Information Systems Research," Journal of the Association for Information Systems, 23(6), 1333-1346. source
  • Morris, Shad, et al. "Theorizing From Emerging Markets: Challenges, Opportunities, and Publishing Advice." Academy of Management Review 48.1 (2023): 1-10. source

Causality and Machine Learning⚓︎

  • Pearl, Judea. "Causal inference in statistics: An overview." Statistics surveys 3 (2009): 96-146. source
  • Schölkopf, Bernhard. "Causality for machine learning." arXiv preprint arXiv:1911.10500 (2019). source
  • Guo, Ruocheng, et al. "A survey of learning causality with data: Problems and methods." ACM Computing Surveys (CSUR) 53.4 (2020): 1-37. source
  • Yao, Liuyi, et al. "A survey on causal inference." arXiv preprint arXiv:2002.02770 (2020). source
  • Schnabel, Tobias, et al. "Recommendations as treatments: Debiasing learning and evaluation." international conference on machine learning. PMLR, 2016. source
  • Bonner, Stephen, and Flavian Vasile. "Causal embeddings for recommendation." Proceedings of the 12th ACM conference on recommender systems. 2018. source
  • Wang, Yixin, et al. "Causal Inference for Recommender Systems." Fourteenth ACM Conference on Recommender Systems. 2020. source
  • Chen, Jiawei, et al. "AutoDebias: Learning to Debias for Recommendation." arXiv preprint arXiv:2105.04170 (2021). source
  • Brett R. Gordon, Robert Moakler, Florian Zettelmeyer (2022) Close Enough? A Large-Scale Exploration of Non-Experimental Approaches to Advertising Measurement. Marketing Science 0(0). source
  • Nicholas P. Danks, Soumya Ray, Galit Shmueli (2023) The Composite Overfit Analysis Framework: Assessing the Out-of-Sample Generalizability of Construct-Based Models Using Predictive Deviance, Deviance Trees, and Unstable Paths. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4705
  • Microsoft Research hosts its causality research at Causality and Machine Learning

Theories⚓︎

  • Gregor, S. (2006). The nature of theory in information systems. MIS quarterly, 611-642. https://doi.org/10.2307/25148742
  • Fink, L. (2021). The Philosopher's Corner: The Role of Theory in Information Systems Research. ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 52(3), 96-103. https://dl.acm.org/doi/10.1145/3481629.3481636
  • Andrade, A, et al (2023) The importance of theory at the Information Systems Journal. Information Systems Journal, editorial. https://doi.org/10.1111/isj.12437

Waiting Cost⚓︎

  • Osuna, Edgar Elias. "The psychological cost of waiting." Journal of Mathematical Psychology 29.1 (1985): 82-105. source

Information Systems Continuance⚓︎

  • Bhattacherjee, Anol. "Understanding information systems continuance: An expectation-confirmation model." MIS quarterly (2001): 351-370. source
  • Soliman, Wael, and Virpi Kristiina Tuunainen. "A tale of two frames: Exploring the role of framing in the use discontinuance of volitionally adopted technology." Information Systems Journal (2021). source
  • Lin, Julian; Yin, Jiamin; Wei, Kwok Kee; Chan, Hock Chuan; and Teo, Hock Hai. 2022. "Comparing Competing Systems: An Extension of the Information Systems Continuance Model," MIS Quarterly, (46: 4) pp.1851-1874. source
  • Lin, Julian; Yin, Jiamin; Wei, Kwok Kee; Chan, Hock Chuan; and Teo, Hock Hai. 2022. "Comparing Competing Systems: An Extension of the Information Systems Continuance Model," MIS Quarterly, (46: 4) pp.1851-1874. source

Expectation-Confirmation Theory⚓︎

  • Oliver, Richard L. "Effect of expectation and disconfirmation on postexposure product evaluations: An alternative interpretation." Journal of applied psychology 62.4 (1977): 480. source
  • Oliver, Richard L. "A cognitive model of the antecedents and consequences of satisfaction decisions." Journal of marketing research 17.4 (1980): 460-469. source

Theory of Acceptance⚓︎

  • Davis, Fred D. "Perceived usefulness, perceived ease of use, and user acceptance of information technology." MIS quarterly (1989): 319-340. source
  • Davis, Fred D., Richard P. Bagozzi, and Paul R. Warshaw. "User acceptance of computer technology: A comparison of two theoretical models." Management science 35.8 (1989): 982-1003. source
  • Taylor, Shirley, and Peter A. Todd. "Understanding information technology usage: A test of competing models." Information systems research 6.2 (1995): 144-176. source
  • Venkatesh, Viswanath, and Fred D. Davis. "A theoretical extension of the technology acceptance model: Four longitudinal field studies." Management science 46.2 (2000): 186-204. source
  • Venkatesh, Viswanath, et al. "User acceptance of information technology: Toward a unified view." MIS quarterly (2003): 425-478. source
  • Dwivedi, Yogesh K., et al. "A meta-analysis based modified unified theory of acceptance and use of technology (meta-UTAUT): a review of emerging literature." Current opinion in psychology 36 (2020): 13-18. source
  • Blut, Markus, et al. "Meta-Analysis of the Unified Theory of Acceptance and Use of Technology (UTAUT): Challenging its Validity and Charting a Research Agenda in the Red Ocean," Journal of the Association for Information Systems (2022), 23(1), 13-95. source
  • Christian Maier, Sven Laumer, Jason Bennett Thatcher, Jakob Wirth, Tim Weitzel (2022) Trial-Period Technostress: A Conceptual Definition and Mixed-Methods Investigation. Information Systems Research 0(0). source

Theory of Planned Behavior⚓︎

Intrinsic Motivation⚓︎

  • Deci, Edward L., and Richard M. Ryan. Intrinsic motivation and self-determination in human behavior. Springer Science & Business Media, 2013. source

Self-Determination Theory⚓︎

  • Deci, Edward L., and Richard M. Ryan. "The" what" and" why" of goal pursuits: Human needs and the self-determination of behavior." Psychological inquiry 11.4 (2000): 227-268. source
  • Ryan, Richard M., and Edward L. Deci. "Intrinsic and extrinsic motivations: Classic definitions and new directions." Contemporary educational psychology 25.1 (2000): 54-67. source
  • Ryan, Richard M., and Edward L. Deci. "Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being." American psychologist 55.1 (2000): 68. source
  • Deci, Edward L., and Richard M. Ryan. Intrinsic motivation and self-determination in human behavior. Springer Science & Business Media, 2013. source

Belief, Attitude, Intention & Behavior⚓︎

  • Fishbein, Martin, and Icek Ajzen. "Belief, attitude, intention, and behavior: An introduction to theory and research." Philosophy and Rhetoric 10.2 (1977). source

Uses and Gratifications⚓︎

  • Ruggiero, Thomas E. "Uses and gratifications theory in the 21st century." Mass communication & society 3.1 (2000): 3-37. source
  • Weiyan, L. I. U. "A historical overview of uses and gratifications theory." Cross-Cultural Communication 11.9 (2015): 71-78. source

Time Allocation⚓︎

  • Becker, Gary S. "A Theory of the Allocation of Time." The economic journal 75.299 (1965): 493-517. source

Social Norms⚓︎

  • Deutsch, Morton, and Harold B. Gerard. "A study of normative and informational social influences upon individual judgment." The journal of abnormal and social psychology 51.3 (1955): 629. source
  • Gibbs, Jack P. "Norms: The problem of definition and classification." American Journal of Sociology 70.5 (1965): 586-594. source
  • Lapinski, Maria Knight, and Rajiv N. Rimal. "An explication of social norms." Communication theory 15.2 (2005): 127-147. source
  • Young, H. Peyton. "The evolution of social norms." economics 7.1 (2015): 359-387. source
  • Legros, Sophie, and Beniamino Cislaghi. "Mapping the social-norms literature: An overview of reviews." Perspectives on Psychological Science 15.1 (2020): 62-80. source
  • Horne, Christine, and Stefanie Mollborn. "Norms: An integrated framework." Annual Review of Sociology 46 (2020): 467-487. source
  • Eugen Dimant (2023) Hate Trumps Love: The Impact of Political Polarization on Social Preferences. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4701

Targeting with Mobile Coupons⚓︎

  • Ghose, Anindya, et al. "Seizing the commuting moment: Contextual targeting based on mobile transportation apps." Information Systems Research 30.1 (2019): 154-174. source
  • Andrews, Michelle, et al. "Mobile ad effectiveness: Hyper-contextual targeting with crowdedness." Marketing Science 35.2 (2016): 218-233. source

Multichannel Advertising and Retailing⚓︎

  • Ghose, Anindya, and Vilma Todri. "Towards a digital attribution model: Measuring the impact of display advertising on online consumer behavior." Available at SSRN 2672090 (2015). source
  • Kumar, Anuj, Amit Mehra, and Subodha Kumar. "Why do stores drive online sales? Evidence of underlying mechanisms from a multichannel retailer." Information Systems Research 30.1 (2019): 319-338. source
  • Che, Tong, et al. "Online prejudice and barriers to digital innovation: Empirical investigations of Chinese consumers." Information Systems Journal (2021). source
  • Wei Chen, Zaiyan Wei, Karen Xie (2022) The Battle for Homes: How Does Home Sharing Disrupt Local Residential Markets?. Management Science 0(0). source
  • Scott K. Shriver, Bryan Bollinger (2022) Demand Expansion and Cannibalization Effects from Retail Store Entry: A Structural Analysis of Multichannel Demand. Management Science 0(0). source

Advertising and Recommendations⚓︎

  • Kumar, Anuj, and Yinliang Tan. "The demand effects of joint product advertising in online videos." Management Science 61.8 (2015): 1921-1937. source
  • Kumar, Anuj, and Kartik Hosanagar. "Measuring the value of recommendation links on product demand." Information Systems Research 30.3 (2019): 819-838. source
  • Matthew McGranaghan, Jura Liaukonyte, Kenneth C. Wilbur (2022) How Viewer Tuning, Presence, and Attention Respond to Ad Content and Predict Brand Search Lift. Marketing Science 0(0). source
  • Adamopoulos, Panagiotis, Anindya Ghose, and Alexander Tuzhilin. "Heterogeneous demand effects of recommendation strategies in a mobile application: Evidence from econometric models and machine-learning instruments." MIS Quarterly (2022). source
  • Tesary Lin, Sanjog Misra (2022) Frontiers: The Identity Fragmentation Bias. Marketing Science 0(0). source
  • Ada, Sıla, Nadia Abou Nabout, and Elea McDonnell Feit. "EXPRESS: Context Information can Increase Revenue in Online Display Advertising Auctions: Evidence from a Policy Change." Journal of Marketing Research (2021). source
  • Rafieian, Omid, and Hema Yoganarasimhan. “Variety Effects in Mobile Advertising.” Journal of Marketing Research, Apr. 2022. source
  • Ghosh Dastidar, A., Sunder, S., & Shah, D. (2022). Societal Spillovers of TV Advertising: Social Distancing During a Public Health Crisis. Journal of Marketing, 0(0). source
  • Weijia Dai, Hyunjin Kim, Michael Luca (2023) Frontiers: Which Firms Gain from Digital Advertising? Evidence from a Field Experiment. Marketing Science 0(0). https://doi.org/10.1287/mksc.2023.1436

Technology⚓︎

Educational Technology⚓︎

  • Kumar, Anuj, and Amit Mehra. "Remedying Education with Personalized Homework: Evidence from a Randomized Field Experiment in India." Available at SSRN 2756059 (2018). source
  • Qiang Gao, Mingfeng Lin, D. J. Wu (2021) Education Crowdfunding and Student Performance: An Empirical Study. Information Systems Research 32(1):53-71. source
  • Samantha M. Keppler, Jun Li, Di (Andrew) Wu (2022) Crowdfunding the Front Lines: An Empirical Study of Teacher-Driven School Improvement. Management Science 0(0). source

Green Technology⚓︎

  • Saldanha, Terence J. V.; Mithas, Sunil; Khuntia, Jiban; Whitaker, Jonathan; and Melville, Nigel P.. 2022. "How Green Information Technology Standards and Strategies Influence Performance: Role of Environment, Cost, and Dual Focus," MIS Quarterly, (46: 4) pp.2367-2386. source
  • Zhiling Guo, Jin Li, Ram Ramesh (2023) Green Data Analytics of Supercomputing from Massive Sensor Networks: Does Workload Distribution Matter?. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1208

Facial Recognition⚓︎

  • Jia Gao, Ying Rong, Xin Tian, Yuliang Yao (2023) Improving Convenience or Saving Face? An Empirical Analysis of the Use of Facial Recognition Payment Technology in Retail. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1205

Voice⚓︎

  • Melzner, J., Bonezzi, A., & Meyvis, T. (2023). Information Disclosure in the Era of Voice Technology. Journal of Marketing, 0(0). https://doi.org/10.1177/00222429221138286

Healthcare⚓︎

  • Elina H. Hwang, Xitong Guo, Yong Tan, Yuanyuan Dang (2022) Delivering Healthcare Through Teleconsultations: Implications for Offline Healthcare Disparity. Information Systems Research 0(0). source
  • Ginger Zhe Jin, Ajin Lee, Susan Feng Lu (2022) Patient Routing to Skilled Nursing Facilities: The Consequences of the Medicare Reimbursement Rule. Management Science 0(0). source
  • Ghose, Anindya, et al. "Empowering patients using smart mobile health platforms: Evidence from a randomized field experiment." MIS Quarterly (2022). source
  • Clary, G., Dick, G., Akbulut, A. Y., & Van Slyke, C. (2022). The After Times: College Students’ Desire to Continue with Distance Learning Post Pandemic. Communications of the Association for Information Systems, 50, pp-pp. source
  • Gorkem Turgut Ozer, Brad N. Greenwood, Anandasivam Gopal (2022) Digital Multisided Platforms and Women’s Health: An Empirical Analysis of Peer-to-Peer Lending and Abortion Rates. Information Systems Research 0(0). source
  • Shelly Rathee, Kritika Narula, Arul Mishra, Himanshu Mishra (2022) Alphanumeric vs. Numeric Token Systems and the Healthcare Experience: Field Evidence from Healthcare Delivery in India. Management Science 0(0). source
  • Sykes, Tracy Ann, and Ruba Aljafari. "We Are All in This Together, or Are We? Job Strain and Coping in the Context of an E-Healthcare System Implementation." Journal of Management Information Systems 39.4 (2022): 1215-1247. https://doi.org/10.1080/07421222.2022.2127450
  • Temidayo Adepoju, Anita L. Carson, Helen S. Jin, Christopher S. Manasseh (2023) Hospital Boarding Crises: The Impact of Urgent vs. Prevention Responses on Length of Stay. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4724
  • Sezgin Ayabakan, Indranil R. Bardhan, Zhiqiang (Eric) Zheng (2023) Impact of Telehealth and Process Virtualization on Healthcare Utilization. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1220
  • Thiebes, S., Gao, F., Briggs, R. O., Schmidt-Kraepelin, M., & Sunyaev, A. (2023). Design Concerns for Multiorganizational, Multistakeholder Collaboration: A Study in the Healthcare Industry. Journal of management information systems, 1. https://doi.org/10.1080/07421222.2023.2172771

Pandemic⚓︎

  • Marta Serra-Garcia, Nora Szech (2022) Incentives and Defaults Can Increase COVID-19 Vaccine Intentions and Test Demand. Management Science 0(0). source
  • Joseph R. Buckman, Idris Adjerid, Catherine Tucker (2022) Privacy Regulation and Barriers to Public Health. Management Science 0(0). source
  • Jean-Philippe Bonardi, Quentin Gallea, Dimitrija Kalanoski, Rafael Lalive (2023) Managing Pandemics: How to Contain COVID-19 Through Internal and External Lockdowns and Their Release. Management Science 0(0). https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2022.4652

Applications of Artificial Intelligence⚓︎

  • Wang, Quan, Beibei Li, and Param Vir Singh. "Copycats vs. original mobile apps: A machine learning copycat-detection method and empirical analysis." Information Systems Research 29.2 (2018): 273-291. source
  • Burtch, Gordon, Anindya Ghose, and Sunil Wattal. "The hidden cost of accommodating crowdfunder privacy preferences: A randomized field experiment." Management Science 61.5 (2015): 949-962. source
  • Vítor Albiero, and Kevin W. Bowyer. "Is Face Recognition Sexist? No, Gendered Hairstyles and Biology Are" BMVC 2020. source
  • Garvey, Aaron M., et al. “Bad News? Send an AI. Good News? Send a Human.” Journal of Marketing, Feb. 2022. source
  • Martin Reisenbichler, Thomas Reutterer, David A. Schweidel, Daniel Dan (2022) Frontiers: Supporting Content Marketing with Natural Language Generation. Marketing Science 0(0). source
  • Andreas Barth, Sasan Mansouri, Fabian Wöbbeking, (2022) “Let Me Get Back to You”—A Machine Learning Approach to Measuring NonAnswers. Management Science 0(0). source

Software⚓︎

Piracy⚓︎

  • Martin Eisend. "Explaining Digital Piracy: A Meta-Analysis." Information Systems Research 30.2 (2019): 636-664. source
  • Christian Peukert, Stefan Bechtold, Michail Batikas, Tobias Kretschmer (2022) Regulatory Spillovers and Data Governance: Evidence from the GDPR. Marketing Science 0(0). source

Cybersecurity⚓︎

  • Kolini, F., & Janczewski, L. J. (2022). Exploring Incentives and Challenges for Cybersecurity Intelligence Sharing (CIS) across Organizations: A Systematic Review. Communications of the Association for Information Systems, 50, pp-pp. source
  • D'Arcy, John and Basoglu, Asli (2022) "The Influences of Public and Institutional Pressure on Firms’ Cybersecurity Disclosures," Journal of the Association for Information Systems, 23(3), 779-805. source
  • A. J. Burns, Tom L. Roberts, Clay Posey, Paul Benjamin Lowry, Bryan Fuller (2022) Going Beyond Deterrence: A Middle-Range Theory of Motives and Controls for Insider Computer Abuse. Information Systems Research 0(0). source
  • Nikkhah, Hamid Reza and Grover, Varun. 2022. "An Empirical Investigation of Company Response to Data Breaches," MIS Quarterly, (46: 4) pp.2163-2196. source

Electronic Participation⚓︎

  • Yo, Y., & Xu, P. (2022). The Power of Electronic Channels and Electronic Political Efficacy: Electronic Participation Discourse. Communications of the Association for Information Systems, 50, pp-pp. source

Productivity⚓︎

  • Peng Huang, Marco Ceccagnoli, Chris Forman, D.J. Wu (2022) IT Knowledge Spillovers, Absorptive Capacity, and Productivity: Evidence from Enterprise Software. Information Systems Research 0(0). source

Software Development⚓︎

  • Gregory Vial (2023) A Complex Adaptive Systems Perspective of Software Reuse in the Digital Age: An Agenda for IS Research. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1200

Algorithm⚓︎

Impact⚓︎

  • Athey, Susan. "The impact of machine learning on economics." The economics of artificial intelligence: An agenda. University of Chicago Press, 2018. 507-547. pdf

Bias⚓︎

  • Lambrecht, Anja, and Catherine Tucker. "Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads." Management Science 65.7 (2019): 2966-2981. source

Aversion⚓︎

  • Dietvorst, Berkeley J., Joseph P. Simmons, and Cade Massey. "Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them." Management Science 64.3 (2018): 1155-1170. source
  • Germann, Maximilian, and Christoph Merkle. "Algorithm Aversion in Financial Investing." Available at SSRN 3364850 (2019). source

Human & Algorithm⚓︎

  • Kleinberg, Jon, et al. "Human decisions and machine predictions." The quarterly journal of economics 133.1 (2018): 237-293. source
  • Liwei Chen, J. J. Po-An Hsieh, Arun Rai (2022) How Does Intelligent System Knowledge Empowerment Yield Payoffs? Uncovering the Adaptation Mechanisms and Contingency Role of Work Experience. Information Systems Research 0(0). source
  • Tarafdar, Monideepa, Xinru Page, and Marco Marabelli. "Algorithms as co‐workers: Human algorithm role interactions in algorithmic work." Information Systems Journal. source
  • Chen, Yang, et al. "Does Techno-invasion Lead to Employees’ Deviant Behaviors?." Journal of Management Information Systems 39.2 (2022): 454-482. source
  • You, Sangseok, Cathy Liu Yang, and Xitong Li. "Algorithmic versus Human Advice: Does Presenting Prediction Performance Matter for Algorithm Appreciation?." Journal of Management Information Systems 39.2 (2022): 336-365. source
  • Ghasemaghaei, Maryam, and Ofir Turel. "Why Do Data Analysts Take IT-Mediated Shortcuts? An Ego-Depletion Perspective." Journal of Management Information Systems 39.2 (2022): 483-512. source
  • Elizabeth Han, Dezhi Yin, Han Zhang (2022) Bots with Feelings: Should AI Agents Express Positive Emotion in Customer Service?. Information Systems Research 0(0). source
  • Mikhail Lysyakov , Siva Viswanathan (2022) Threatened by AI: Analyzing Users’ Responses to the Introduction of AI in a Crowd-Sourcing Platform. Information Systems Research 0(0). source
  • Nasim Mousavi, Panagiotis Adamopoulos, Jesse Bockstedt (2023) The Decoy Effect and Recommendation Systems. Information Systems Research 0(0). https://doi.org/10.1287/isre.2022.1197
  • Callen Anthony, Beth A. Bechky, Anne-Laure Fayard (2023) “Collaborating” with AI: Taking a System View to Explore the Future of Work. Organization Science 0(0). https://doi.org/10.1287/orsc.2022.1651
  • Chandra, Shalini, Anuragini Shirish, and Shirish C. Srivastava. "To Be or Not to Be… Human? Theorizing the Role of Human-Like Competencies in Conversational Artificial Intelligence Agents." Journal of Management Information Systems 39.4 (2022): 969-1005. https://doi.org/10.1080/07421222.2022.2127441
  • Tarafdar, Monideepa, Xinru Page, and Marco Marabelli. "Algorithms as co‐workers: Human algorithm role interactions in algorithmic work." Information Systems Journal (2022). https://doi.org/10.1111/isj.12389
  • Kevin Bauer, Moritz von Zahn, Oliver Hinz (2023) Expl(AI)ned: The Impact of Explainable Artificial Intelligence on Users’ Information Processing. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1199
  • Parker, Sara, and Derek Ruths. "Is hate speech detection the solution the world wants?." Proceedings of the National Academy of Sciences 120.10 (2023): e2209384120. https://doi.org/10.1073/pnas.2209384120
  • Chandra, Shalini, Anuragini Shirish, and Shirish C. Srivastava. "To Be or Not to Be… Human? Theorizing the Role of Human-Like Competencies in Conversational Artificial Intelligence Agents." Journal of Management Information Systems 39.4 (2022): 969-1005. https://doi.org/10.1080/07421222.2022.2127441
  • Boyacı, Tamer, Caner Canyakmaz, and Francis de Véricourt. "Human and Machine: The Impact of Machine Input on Decision Making Under Cognitive Limitations." Management Science (2023). https://doi.org/10.1287/mnsc.2023.4744
  • Dolata, M., Katsiuba, D., Wellnhammer, N., & Schwabe, G. (2023). Learning with Digital Agents: An Analysis based on the Activity Theory. Journal of Management Information Systems, 40(1), 56-95. https://doi.org/10.1080/07421222.2023.2172775

Gender⚓︎

  • Lin, Chen, et al. "Do "Little Emperors” Get More Than “Little Empresses"? Boy-Girl Gender Discrimination as Evidenced by Consumption Behavior of Chinese Households." Marketing Science (2021). source
  • Helena Fornwagner, Monika Pompeo, Nina Serdarevic (2022) Choosing Competition on Behalf of Someone Else. Management Science 0(0). source
  • Emilio J. Castilla, Hye Jin Rho (2023) The Gendering of Job Postings in the Online Recruitment Process. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4674
  • Eliot L. Sherman, Raina Brands, Gillian Ku (2023) Dropping Anchor: A Field Experiment Assessing a Salary History Ban with Archival Replication. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4658
  • Zhiyan Wu, Lucia Naldi, Karl Wennberg, Timur Uman (2023) Learning from Their Daughters: Family Exposure to Gender Disparity and Female Representation in Male-Led Ventures. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4727

Privacy⚓︎

  • Godinho de Matos, Miguel, and Idris Adjerid. "Consumer consent and firm targeting after GDPR: The case of a large telecom provider." Management Science (2021). source
  • Heng Xu, Nan Zhang (2022) From Contextualizing to Context Theorizing: Assessing Context Effects in Privacy Research. Management Science 0(0). source
  • Karwatzki, Sabrina et al. The multidimensional nature of privacy risks: Conceptualisation, measurement and implications for digital services. Information Systems Journal (2022). source
  • Tesary Lin (2022) Valuing Intrinsic and Instrumental Preferences for Privacy. Marketing Science 0(0). source
  • Tawfiq Alashoor, Mark Keil, H. Jeff Smith, Allen R. McConnell (2022) Too Tired and in Too Good of a Mood to Worry About Privacy: Explaining the Privacy Paradox Through the Lens of Effort Level in Information Processing. Information Systems Research 0(0). source
  • Ram D. Gopal, Hooman Hidaji, Sule Nur Kutlu, Raymond A. Patterson, Niam Yaraghi (2023) Law, Economics, and Privacy: Implications of Government Policies on Website and Third-Party Information Sharing. Information Systems Research 0(0). https://doi.org/10.1287/isre.2022.1178
  • Garrett A. Johnson, Scott K. Shriver, Samuel G. Goldberg (2023) Privacy and Market Concentration: Intended and Unintended Consequences of the GDPR. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4709

Online Platforms⚓︎

Subscription Models⚓︎

  • Oestreicher-Singer, Gal, and Lior Zalmanson. "Content or community? A digital business strategy for content providers in the social age." MIS quarterly (2013): 591-616. source
  • Bapna, Ravi, and Akhmed Umyarov. "Do your online friends make you pay? A randomized field experiment on peer influence in online social networks." Management Science 61.8 (2015): 1902-1920. source
  • Hongfei Li, Jing Peng, Xinxin Li, Jan Stallaert (2022) When More Can Be Less: The Effect of Add-On Insurance on the Consumption of Professional Services. Information Systems Research 0(0). source

Digital Content & User-Generated Content⚓︎

  • Ye, Hua, et al. "Monetization of Digital Content: Drivers of Revenue on Q&A Platforms." Journal of Management Information Systems 38.2 (2021): 457-483. source
  • Zhiyu Zeng, Hengchen Dai, Dennis J. Zhang, Heng Zhang, Renyu Zhang, Zhiwei Xu, Zuo-Jun Max Shen (2022) The Impact of Social Nudges on User-Generated Content for Social Network Platforms. Management Science 0(0). source
  • Lu, S., Dinner, I., & Grewal, R. (2023). The Ripple Effect of Firm-Generated Content on New Movie Releases. Journal of Marketing Research, 0(0). https://doi.org/10.1177/00222437221143066

Online Reviews⚓︎

  • Chen, Yan, et al. "Social comparisons and contributions to online communities: A field experiment on movielens." American Economic Review 100.4 (2010): 1358-98. source
  • Gordon Burtch, Yili Hong, Ravi Bapna, Vladas Griskevicius (2017) Stimulating Online Reviews by Combining Financial Incentives and Social Norms. Management Science 64(5):2065-2082. source
  • Limin Fang (2022) The Effects of Online Review Platforms on Restaurant Revenue, Consumer Learning, and Welfare. Management Science 0(0). source
  • Jinghui (Jove) Hou, Xiao Ma (2022) Space Norms for Constructing Quality Reviews on Online Consumer Review Sites. Information Systems Research 0(0). source
  • Sherry He, Brett Hollenbeck, Davide Proserpio (2022) The Market for Fake Reviews. Marketing Science 0(0). source
  • Honglin Deng, Weiquan Wang, Siyuan Li, and Kai H. Lim. "Can Positive Online Social Cues Always Reduce User Avoidance of Sponsored Search Results?." MIS Quarterly (2021). source
  • Mengxia Zhang, Lan Luo (2022) Can Consumer-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp. Management Science 0(0). source
  • Choi, HanByeol Stella, et al. "Effects of Online Crowds on Self-Disclosure Behaviors in Online Reviews: A Multidimensional Examination." Journal of Management Information Systems 39.1 (2022): 218-246. source
  • T. Ravichandran, Chaoqun Deng (2022) Effects of Managerial Response to Negative Reviews on Future Review Valence and Complaints. Information Systems Research 0(0). source
  • Jung, M., Ryu, S., Han, S. P., & Cho, D. (2023). Ask for Reviews at the Right Time: Evidence from Two Field Experiments. Journal of Marketing, 0(0). https://doi.org/10.1177/00222429221143329
  • Chen, Y., & Lee, S. (2023). User-Generated Physician Ratings and Their Effects on Patients’ Physician Choices: Evidence from Yelp. Journal of Marketing, 0(0). https://doi.org/10.1177/00222429221146511
  • Uttara Ananthakrishnan, Davide Proserpio, Siddhartha Sharma (2023) I Hear You: Does Quality Improve with Customer Voice?. Marketing Science 0(0). https://doi.org/10.1287/mksc.2023.1437
  • Andrey Fradkin, David Holtz (2023) Do Incentives to Review Help the Market? Evidence from a Field Experiment on Airbnb. Marketing Science 0(0). https://doi.org/10.1287/mksc.2023.1439

Digital Beauty Filter⚓︎

Platform Growth, Merge and Acquisition⚓︎

  • Chiara Farronato, Jessica Fong, Andrey Fradkin (2023) Dog Eat Dog: Balancing Network Effects and Differentiation in a Digital Platform Merger. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4675

Social Media⚓︎

  • Xu, Haifeng, et al (2022) "Why Are People Addicted to SNS? Understanding the Role of SNS Characteristics in the Formation of SNS Addiction," Journal of the Association for Information Systems, 23(3), 806-837. source
  • Peng, Jing, Juheng Zhang, and Ram Gopal. "The Good, the Bad, and the Social Media: Financial Implications of Social Media Reactions to Firm-Related News." Journal of Management Information Systems 39.3 (2022): 706-732. source

Interactions⚓︎

  • Matook, Sabine, Alan R. Dennis, and Yazhu Maggie Wang. "User comments in social media firestorms: A mixed-method study of purpose, tone, and motivation." Journal of Management Information Systems 39.3 (2022): 673-705. source
  • Lu, Yingda; Wu, Junjie; Tan, Yong; and Chen, Jian. 2022. "Microblogging Replies and Opinion Polarization: A Natural Experiment," MIS Quarterly, (46: 4) pp.1901-1936. source
  • Yun Young Hur, Fujie Jin, Xitong Li, Yuan Cheng, Yu Jeffrey Hu (2022) Does Social Influence Change with Other Information Sources? A Large-Scale Randomized Experiment in Medical Crowdfunding. Information Systems Research 0(0). source
  • Wakefield, R. L., & Wakefield, K. (2022). The antecedents and consequences of intergroup affective polarisation on social media. Information Systems Journal, 1– 29. source
  • Miller, Stacy, et al. "Integrating truth bias and elaboration likelihood to understand how political polarisation impacts disinformation engagement on social media." Information Systems Journal (2022). source
  • Wang, Lin, Chong Wang, and Xinyan Yao. "Befriended to polarise? The impact of friend identity on review polarisation—A quasi‐experiment." Information Systems Journal. https://doi.org/10.1111/isj.12425

Fake News on Social Media⚓︎

  • Wang, Shuting, Min-Seok Pang, and Paul A. Pavlou. "Cure or Poison? Identity Verification and the Posting of Fake News on Social Media." Journal of Management Information Systems 38.4 (2021): 1011-1038. source
  • Horner, Christy Galletta, et al. "Emotions: The Unexplored Fuel of Fake News on Social Media." Journal of Management Information Systems 38.4 (2021): 1039-1066. source
  • Deng, Bingjie, and Michael Chau. "The Effect of the Expressed Anger and Sadness on Online News Believability." Journal of Management Information Systems 38.4 (2021): 959-988. source
  • Turel, Ofir, and Babajide Osatuyi. "Biased Credibility and Sharing of Fake News on Social Media: Considering Peer Context and Self-Objectivity State." Journal of Management Information Systems 38.4 (2021): 931-958. source
  • Ng, Ka Chung, Jie Tang, and Dongwon Lee. "The Effect of Platform Intervention Policies on Fake News Dissemination and Survival: An Empirical Examination." Journal of Management Information Systems 38.4 (2021): 898-930. source
  • George, Jordana, Natalie Gerhart, and Russell Torres. "Uncovering the Truth about Fake News: A Research Model Grounded in Multi-Disciplinary Literature." Journal of Management Information Systems 38.4 (2021): 1067-1094. source
  • Gimpel, Henner, et al. "The effectiveness of social norms in fighting fake news on social media." Journal of Management Information Systems 38.1 (2021): 196-221. source
  • Mohamed Mostagir, Asuman Ozdaglar, James Siderius (2022) When Is Society Susceptible to Manipulation?. Management Science 0(0). source
  • Jackie London Jr., Siyuan Li, Heshan Sun (2022) Seems Legit: An Investigation of the Assessing and Sharing of Unverifiable Messages on Online Social Networks. Information Systems Research 0(0). source
  • Mohamed Mostagir, James Siderius (2022) Learning in a Post-Truth World. Management Science 0(0). source
  • Wang, Shuting (Ada); Pang, Min-Seok; and Pavlou, Paul A.. 2022. "Seeing Is Believing? How Including a Video in Fake News Influences Users’ Reporting of Fake News to Social Media Platforms," MIS Quarterly, (46: 3) pp.1323-1354. source
  • Gizem Ceylan, Ian A. Anderson, and Wendy Wood. 2022. "Sharing of misinformation is habitual, not just lazy or biased," PNAS, (120:4) https://doi.org/10.1073/pnas.2216614120

Social Media Marketing⚓︎

  • Tingting Nian, Arun Sundararajan (2022) Social Media Marketing, Quality Signaling, and the Goldilocks Principle. Information Systems Research 0(0). source
  • Jens Foerderer, Sebastian W. Schuetz (2022) Data Breach Announcements and Stock Market Reactions: A Matter of Timing?. Management Science 0(0). source
  • Naveen Kumar, Liangfei Qiu, Subodha Kumar (2022) A Hashtag Is Worth a Thousand Words: An Empirical Investigation of Social Media Strategies in Trademarking Hashtags. Information Systems Research 0(0). source
  • Venkatesan, Srikanth, et al. "INFLUENCE IN SOCIAL MEDIA: AN INVESTIGATION OF TWEETS SPANNING THE 2011 EGYPTIAN REVOLUTION." MIS Quarterly 45.4 (2021). source
  • Alibakhshi, Reza, and Shirish C. Srivastava. "Post-Story: Influence of Introducing Story Feature on Social Media Posts." Journal of Management Information Systems 39.2 (2022): 573-601. source
  • Weiler, Michael, et al. " Social Capital Accumulation Through Social Media Networks: Evidence from a Randomized Field Experiment and Individual-Level Panel Data," Management Information Systems Quarterly, (2021). source
  • Leung, Fine F., et al. "Influencer Marketing Effectiveness." Journal of Marketing (2022) source
  • Liadeli, G., Sotgiu, F., & Verlegh, P. W. J. (2022). A Meta-Analysis of the Effects of Brands’ Owned Social Media on Social Media Engagement and Sales. Journal of Marketing, 0(0). source
  • Woolley, K., Kupor, D., & Liu, P. J. (2022). Does Company Size Shape Product Quality Inferences? Larger Companies Make Better High-Tech Products, but Smaller Companies Make Better Low-Tech Products. Journal of Marketing Research, 0(0). source

Norms And Roles⚓︎

  • Emmanuelle Vaast, Alain Pinsonneault (2022) Dealing with the Social Media Polycontextuality of Work. Information Systems Research 0(0). source
  • Verena Schoenmueller, Oded Netzer, Florian Stahl (2022) Frontiers: Polarized America: From Political Polarization to Preference Polarization. Marketing Science 0(0). source

Social Investing⚓︎

  • Jake An, Donnel Briley, Shai Danziger, Shai Levi (2022) The Impact of Social Investing on Charitable Donations. Management Science 0(0). source

Network & Graph⚓︎

  • Mariia Petryk, Michael Rivera, Siddharth Bhattacharya, Liangfei Qiu, Subodha Kumar (2022) How Network Embeddedness Affects Real-Time Performance Feedback: An Empirical Investigation. Information Systems Research 0(0). source
  • Rohit Aggarwal, Vishal Midha, Nicholas Sullivan (2023) Effect of Online Professional Network Recommendations on the Likelihood of an Interview: A Field Study. Information Systems Research 0(0). https://doi.org/10.1287/isre.2021.1053
  • Rohit Aggarwal, Vishal Midha, Nicholas Sullivan (2023) The Effect of Gender Expectations and Physical Attractiveness on Discussion of Weakness in Online Professional Recommendations. Information Systems Research 0(0). https://doi.org/10.1287/isre.2021.1032

Content Consumption & Sharing⚓︎

  • Hyelim Oh, Khim-Yong Goh, Tuan Q. Phan (2022) Are You What You Tweet? The Impact of Sentiment on Digital News Consumption and Social Media Sharing. Information Systems Research 0(0). source
  • Barnea, U., Meyer, R. J., & Nave, G. (2023). The Effects of Content Ephemerality on Information Processing. Journal of Marketing Research, 0(0). https://doi.org/10.1177/00222437221131047

News⚓︎

  • O’Riordan, S., Emerson, B., Feller, J., & Kiely, G. (2023). The Road to Open News: A Theory of Social Signaling in an Open News Production Community. Journal of Management Information Systems, 40(1), 130-162. https://doi.org/10.1080/07421222.2023.2172777

E-Commerce & Online Shopping⚓︎

  • McKnight, D. Harrison, Vivek Choudhury, and Charles Kacmar. "Developing and validating trust measures for e-commerce: An integrative typology." Information systems research 13.3 (2002): 334-359. source
  • Shang, Rong-An, Yu-Chen Chen, and Lysander Shen. "Extrinsic versus intrinsic motivations for consumers to shop on-line." Information & management 42.3 (2005): 401-413. source
  • Kim, Hee-Woong, Hock Chuan Chan, and Atreyi Kankanhalli. "What motivates people to purchase digital items on virtual community websites? The desire for online self-presentation." Information systems research 23.4 (2012): 1232-1245. source
  • Pavlou, Paul A. "Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model." International journal of electronic commerce 7.3 (2003): 101-134. source
  • Arvind K. Tripathi, Young-Jin Lee, Amit Basu (2022) Analyzing the Impact of Public Buyer–Seller Engagement During Online Auctions. Information Systems Research 0(0). source
  • Khan, A., & Krishnan, S. (2022). Ethical Behavior of Firms and B2C E-commerce Diffusion: Exploring the Mediating Roles of Customer Orientation and Innovation Capacity. Communications of the Association for Information Systems, 50, pp-pp. source
  • Iyengar R, Park Y-H, Yu Q. The Impact of Subscription Programs on Customer Purchases. Journal of Marketing Research. 2022. source
  • Yufeng Huang, Bart J. Bronnenberg (2022) Consumer Transportation Costs and the Value of E-Commerce: Evidence from the Dutch Apparel Industry. Marketing Science 0(0). source
  • Bei, Z., & Gielens, K. (2022). The One-Party Versus Third-Party Platform Conundrum: How Can Brands Thrive? Journal of Marketing, 0(0). source
  • Deng, Honglin; Wang, Weiquan; and Lim, Kai H.. 2022. "Repairing Integrity-Based Trust Violations in Ascription Disputes for Potential E-Commerce Customers," MIS Quarterly, (46: 4) pp.1983-2014. source
  • Murat Unal, Young-Hoon Park (2023) Fewer Clicks, More Purchases. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4716
  • Daniel W. Elfenbein, Raymond Fisman, Brian McManus (2023) The Impact of Socioeconomic and Cultural Differences on Online Trade. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4681
  • Yue Guan, Yong Tan, Qiang Wei, Guoqing Chen (2023) When Images Backfire: The Effect of Customer-Generated Images on Product Rating Dynamics. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1201
  • Ren, Fei; Tan, Yong; and Wan, Fei. 2023. "Know Your Firm: Managing Social Media Engagement to Improve Firm Sales Performance," MIS Quarterly, (47: 1) pp.227-262. https://aisel.aisnet.org/misq/vol47/iss1/10/
  • Geneviève Bassellier, Jui Ramaprasad (2023) All External Reference Prices Are Not the Same: How Magnitude, Source, and Fairness Shape Payment for Digital Goods. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1206
  • Ilya Morozov (2023) Measuring Benefits from New Products in Markets with Information Frictions. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4729
  • KC, R. P., Mak, V., & Ofek, E. (2023). Before or After? The Effects of Payment Decision Timing in Pay-What-You-Want Contexts. Journal of Marketing, 0(0). https://doi.org/10.1177/00222429221142234

Crowdfunding Markets⚓︎

  • Yan Xu, Jian Ni (2022) Entrepreneurial Learning and Disincentives in Crowdfunding Markets. Management Science 0(0). source
  • Kao, Ta-Wei, et al. "Deriving Execution Effectiveness of Crowdfunding Projects from the Fundraiser Network." Journal of Management Information Systems 39.1 (2022): 276-301. source
  • Rhue, Lauren and Clark, Jessica. 2022. "Who Are You and What Are You Selling? Creator-Based and Product-Based Racial Cues in Crowdfunding," MIS Quarterly, (46: 4) pp.2229-2260. source
  • Markus Weinmann, Abhay Nath Mishra, Lena Franziska Kaiser, Jan vom Brocke (2022) The Attraction Effect in Crowdfunding. Information Systems Research 0(0). source
  • Lin, Mingfeng, et al. "# Experts vs. Non-Experts in Online Crowdfunding Markets." Management Information Systems Quarterly 47.1 (2023): 97-126. https://aisel.aisnet.org/misq/vol47/iss1/6

Crowdsourcing⚓︎

  • Cao, Fang, et al. "Do Social Dominance-Based Faultlines Help or Hurt Team Performance in Crowdsourcing Tournaments?." Journal of Management Information Systems 39.1 (2022): 247-275. source
  • Deodhar, Swanand J.; Babar, Yash; and Burtch, Gordon. 2022. "The Influence of Status on Evaluations: Evidence from Online Coding Contests," MIS Quarterly, (46: 4) pp.2085-2110. source
  • Yan, Bei, and Andrea B. Hollingshead. "Motivating the Motivationally Diverse Crowd: Social Value Orientation and Reward Structure in Crowd Idea Generation." Journal of Management Information Systems 39.4 (2022): 1064-1088. https://doi.org/10.1080/07421222.2022.2127451

Social Reputation⚓︎

  • Ohanian, Roobina. "Construction and validation of a scale to measure celebrity endorsers' perceived expertise, trustworthiness, and attractiveness." Journal of advertising 19.3 (1990): 39-52. source
  • Swanand J. Deodhar, Samrat Gupta (2022) The Impact of Social Reputation Features in Innovation Tournaments: Evidence from a Natural Experiment. Information Systems Research 0(0). source
  • David Keith, Lauren Taylor, James Paine, Richard Weisbach, Anthony Dowidowicz (2022) When Funders Aren’t Customers: Reputation Management and Capability Underinvestment in Multiaudience Organizations. Organization Science 0(0). source

Waiting & Delays⚓︎

  • Taylor, Shirley. "Waiting for service: the relationship between delays and evaluations of service." Journal of marketing 58.2 (1994): 56-69. source
  • Dellaert, Benedict GC, and Barbara E. Kahn. "How tolerable is delay?: Consumers’ evaluations of internet web sites after waiting." Journal of interactive marketing 13.1 (1999): 41-54. source
  • Hoxmeier, John A., and Chris DiCesare. "System response time and user satisfaction: An experimental study of browser-based applications." (2000). source
  • Weinberg, Bruce D. "Don't keep your internet customers waiting too long at the (virtual) front door." Journal of interactive marketing 14.1 (2000): 30-39. source
  • Galletta, Dennis F., et al. "Web site delays: How tolerant are users?." Journal of the Association for Information Systems 5.1 (2004): 1. source
  • Nah, Fiona Fui-Hoon. "A study on tolerable waiting time: how long are web users willing to wait?." Behaviour & Information Technology 23.3 (2004): 153-163. source
  • Lee, Younghwa, Andrew NK Chen, and Virginia Ilie. "Can online wait be managed? The effect of filler interfaces and presentation modes on perceived waiting time online." Mis Quarterly (2012): 365-394. source

Word of Mouth And Behavior⚓︎

  • Kunst, Katrine, Torsten Ringberg, and Ravi Vatrapu. "Beyond popularity: A user perspective on observable behaviours in a digital platform." Information Systems Journal (2021). source
  • Tao Lu, May Yuan, Chong (Alex) Wang, Xiaoquan (Michael) Zhang (2022) Histogram Distortion Bias in Consumer Choices. Management Science 0(0). source
  • Sabzehzar, Amin, et al. "Putting Religious Bias in Context: How Offline and Online Contexts Shape Religious Bias in Online Prosocial Lending." Management Information Systems Quarterly 47.1 (2023): 33-62. https://aisel.aisnet.org/misq/vol47/iss1/4

Live Stream⚓︎

  • Guan, Zhengzhi, et al. "What influences the purchase of virtual gifts in live streaming in China? A cultural context‐sensitive model." Information Systems Journal (2021). source
  • Sim, Jaeung, et al. "In-Consumption Information Cues and Digital Content Demand: Evidence from a Live-Streaming Platform." Available at SSRN 3922723 (2021). source
  • Brett, Noel. "Why Do We Only Get Anime Girl Avatars? Collective White Heteronormative Avatar Design in Live Streams." Television & New Media (2022): source

Collaboration⚓︎

- Li, He, Chen Zhang, and William J. Kettinger. "DIGITAL PLATFORM ECOSYSTEM DYNAMICS: THE ROLES OF PRODUCT SCOPE, INNOVATION, AND COLLABORATIVE NETWORK CENTRALITY." MIS Quarterly 46.2 (2022). source⚓︎

Communities⚓︎

  • Li, Yang-Jun; Cheung, Christy M.K; Shen, Xiao-Liang; and Lee, Matthew K. O. (2022) "When Socialization Goes Wrong: Understanding the We-Intention to Participate in Collective Trolling in Virtual Communities," Journal of the Association for Information Systems, 23(3), 678-706. source
  • Zhou, Jiaqi, et al. "Unintended Emotional Effects of Online Health Communities: A Text Mining-Supported Empirical Study." Management Information Systems Quarterly 47.1 (2023): 195-226. https://aisel.aisnet.org/misq/vol47/iss1/9

Online Dating⚓︎

  • Ravi Bapna, Edward McFowland, III, Probal Mojumder, Jui Ramaprasad, Akhmed Umyarov (2022) So, Who Likes You? Evidence from a Randomized Field Experiment. Management Science 0(0). source

Generativity⚓︎

  • Daniel Fürstenau, Abayomi Baiyere, Kai Schewina, Matthias Schulte-Althoff, Hannes Rothe (2023) Extended Generativity Theory on Digital Platforms. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1209

Video Games⚓︎

  • Michael, David R., and Sandra L. Chen. Serious games: Games that educate, train, and inform. Muska & Lipman/Premier-Trade, 2005.
  • Le Wang, Yongqiang Sun, Xin (Robert) Luo. (2022) "Game affordance, gamer orientation, and in-game purchases: A hedonic–instrumental framework," Information Systems Journal. source
  • Le Wang, Paul Benjamin Lowry, Xin (Robert) Luo, Han Li (2022) Moving Consumers from Free to Fee in Platform-Based Markets: An Empirical Study of Multiplayer Online Battle Area Games. Information Systems Research 0(0). source

Gamification⚓︎

  • Zichermann, Gabe, and Christopher Cunningham. Gamification by design: Implementing game mechanics in web and mobile apps. " O'Reilly Media, Inc.", 2011.
  • Huotari, Kai, and Juho Hamari. "Defining gamification: a service marketing perspective." Proceeding of the 16th international academic MindTrek conference. 2012. source
  • Hamari, Juho, Jonna Koivisto, and Harri Sarsa. "Does gamification work?--a literature review of empirical studies on gamification." 2014 47th Hawaii international conference on system sciences. Ieee, 2014. source
  • Seaborn, Katie, and Deborah I. Fels. "Gamification in theory and action: A survey." International Journal of human-computer studies 74 (2015): 14-31. source
  • Koivisto, Jonna, and Juho Hamari. "The rise of motivational information systems: A review of gamification research." International Journal of Information Management 45 (2019): 191-210. source
  • Behnaz Bojd, Xiaolong Song, Yong Tan, Xiangbin Yan (2022) Gamified Challenges in Online Weight-Loss Communities. Information Systems Research 0(0). source
  • Kwak, Dong-Heon; Deng, Shuyuan; Kuem, Jungwon; and Kim, Sung S. (2022) "How to Achieve Goals in Digital Games: An Empirical Test of a Goal-Oriented Model in Pokémon GO," Journal of the Association for Information Systems, 23(2), 553-588. source
  • Alvin Chung Man Leung, Radhika Santhanam, Ron Chi-Wai Kwok, Wei Thoo Yue (2022) Could Gamification Designs Enhance Online Learning Through Personalization? Lessons from a Field Experiment. Information Systems Research 0(0). source
  • Jensen, Matthew L., et al. "Improving Phishing Reporting Using Security Gamification." Journal of Management Information Systems 39.3 (2022): 793-823. source
  • Muhammad Zia Hydari, Idris Adjerid, Aaron D. Striegel (2022) Health Wearables, Gamification, and Healthful Activity. Management Science 0(0). source

Video Game Live-streaming⚓︎

  • Li, Yi, Chongli Wang, and Jing Liu. "A systematic review of literature on user behavior in video game live streaming." International Journal of Environmental Research and Public Health 17.9 (2020): 3328. source
  • Simon Bründl, Christian Matt, Thomas Hess & Simon Engert (2022) How Synchronous Participation Affects the Willingness to Subscribe to Social Live Streaming Services: The Role of Co-Interactive Behavior on Twitch, European Journal of Information Systems source

Video Games & Mental Health⚓︎

  • Cheng, Zhi, Brad N. Greenwood, and Paul A. Pavlou. "Location-based mobile gaming and local depression trends: a study of Pokémon Go." Journal of Management Information Systems 39.1 (2022): 68-101. source

Peer Influence⚓︎

  • Jung, JaeHwuen, et al. "Words Matter! Toward a Prosocial Call-to-Action for Online Referral: Evidence from Two Field Experiments." Information Systems Research (2020). source
  • Bryan Bollinger, Kenneth Gillingham, A. Justin Kirkpatrick, Steven Sexton (2022) Visibility and Peer Influence in Durable Good Adoption. Marketing Science 0(0). source
  • Rodrigo Belo, Ting Li (2022) Social Referral Programs for Freemium Platforms. Management Science 0(0). source
  • Pyo T-H, Lee JY, Park HM. The Effects of Consumer Preference and Peer Influence on Trial of an Experience Good. Journal of Marketing Research. May 2022. source

Consumer Preference⚓︎

  • Pao-Li Chang, Tomoki Fujii, Wei Jin (2022) Good Names Beget Favors: The Impact of Country Image on Trade Flows and Welfare. Management Science 0(0). source
  • Andrew Meyer, Sean Hundtofte (2023) The Longshot Bias Is a Context Effect. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4684

Labor & Market⚓︎

Labor Supply⚓︎

  • Hai Long Duong, Junhong Chu, Dai Yao (2022) Taxi Drivers’ Response to Cancellations and No-Shows: New Evidence for Reference-Dependent Preferences. Management Science 0(0). source
  • Mithas, Sunil; Chen, Yanzhen; Liu, Che-Wei; and Han, Kunsoo. 2022. "Are Foreign and Domestic Information Technology Professionals Complements or Substitutes?," MIS Quarterly, (46: 4) pp.2351-2366. source
  • Luxi Shen, Samuel D. Hirshman (2022) As Wages Increase, Do People Work More or Less? A Wage Frame Effect. Management Science 0(0). source
  • Sudheer Chava, Alexander Oettl, Manpreet Singh (2023) Does a One-Size-Fits-All Minimum Wage Cause Financial Stress for Small Businesses?. Management Science 0(0). source
  • Mitch Downey, Nelson Lind, Jeffrey G. Shrader (2023) Adjusting to Rain Before It Falls. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4697

Work Schedule Improvement⚓︎

  • Saravanan Kesavan, Susan J. Lambert, Joan C. Williams, Pradeep K. Pendem (2022) Doing Well by Doing Good: Improving Retail Store Performance with Responsible Scheduling Practices at the Gap, Inc.. Management Science 0(0). source

Gig Economy⚓︎

  • Yanhui Wu, Feng Zhu (2022) Competition, Contracts, and Creativity: Evidence from Novel Writing in a Platform Market. Management Science 0(0). source

Conflict with Work⚓︎

  • Massimo Magni, Manju K. Ahuja, Chiara Trombini (2022) Excessive Mobile Use and Family-Work Conflict: A Resource Drain Theory Approach to Examine Their Effects on Productivity and Well-Being. Information Systems Research 0(0). source

Cyberloafing⚓︎

  • Qian Chen, et al (2022) How mindfulness decreases cyberloafing at work: a dual-system theory perspective, European Journal of Information Systems. source

Career⚓︎

  • Deng, X., Zaza, S., & Armstrong, D. J. (2023). What Motivates First-generation College Students to Consider an IT Career? An Integrative Perspective. Communications of the Association for Information Systems, 52, pp-pp. source

Wage⚓︎

  • Sumit Agarwal, Meghana Ayyagari, Renáta Kosová (2023) Minimum Wage Increases and Employer Performance: Role of Employer Heterogeneity. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4650

Service Operations⚓︎

  • Andres Musalem, Marcelo Olivares, Daniel Yung (2023) Balancing Agent Retention and Waiting Time in Service Platforms. Operations Research 0(0). https://doi.org/10.1287/opre.2022.2418

Matching Markets⚓︎

  • Lanfei Shi, Siva Viswanathan (2023) Optional Verification and Signaling in Online Matching Markets: Evidence from a Randomized Field Experiment. Information Systems Research 0(0). https://doi.org/10.1287/isre.2022.1194

Addiction⚓︎

  • Isaac Vaghefi, Bogdan Negoita, Liette Lapointe (2022) The Path to Hedonic Information System Use Addiction: A Process Model in the Context of Social Networking Sites. Information Systems Research 0(0). source

Abuse⚓︎

  • Amo, Laura C,; Grijalva, Emily; Herath, Tejaswini; Lemoine, G. James; and Rao, H. Raghav. 2022. "Technological Entitlement: It’s My Technology and I’ll (Ab)Use It How I Want To," MIS Quarterly, (46: 3) pp.1395-1420. source

Bayesian Belief⚓︎

  • Markus M. Möbius, Muriel Niederle, Paul Niehaus, Tanya S. Rosenblat (2022) Managing Self-Confidence: Theory and Experimental Evidence. Management Science 0(0). source
  • Stefanie Brilon, Simona Grassi, Manuel Grieder, Jonathan F. Schulz (2023) Strategic Competition and Self-Confidence. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4688
  • Jeremy Watson, Megan MacGarvie, John McKeon (2022) It Was 50 Years Ago Today: Recording Copyright Term and the Supply of Music. Management Science 0(0). source

Sports⚓︎

  • Bouke Klein Teeselink, Martijn J. van den Assem, Dennie van Dolder (2022) Does Losing Lead to Winning? An Empirical Analysis for Four Sports. Management Science 0(0). source

Financial Technology (Fintech)⚓︎

  • Christoph Herpfer, Aksel Mjøs, Cornelius Schmidt (2022) The Causal Impact of Distance on Bank Lending. Management Science 0(0). source
  • Ng, Evelyn, et al. "The strategic options of fintech platforms: An overview and research agenda." Information Systems Journal (2022). https://doi.org/10.1111/isj.12388
  • Maggie Rong Hu, Xiaoyang Li, Yang Shi, Xiaoquan (Michael) Zhang (2023) Numerological Heuristics and Credit Risk in Peer-to-Peer Lending. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1202

Decision Making⚓︎

  • Joshua Lewis, Daniel Feiler, Ron Adner (2022) The Worst-First Heuristic: How Decision Makers Manage Conjunctive Risk. Management Science 0(0). source
  • Huang, L., & Savary, J. (2022). When Payments Go Social: The Use  of Person-to-Person Payment Methods Attenuates the Endowment Effect. Journal of Marketing Research, 0(0). source
  • Elif Incekara-Hafalir, Grace H. Y. Lee, Audrey K. L. Siah, Erte Xiao (2023) Incentives to Persevere. Management Science 0(0). source
  • Carlos Alós-Ferrer, Michele Garagnani (2023) Part-Time Bayesians: Incentives and Behavioral Heterogeneity in Belief Updating. Management Science 0(0). source
  • Geoffrey Fisher (2023) Measuring the Factors Influencing Purchasing Decisions: Evidence From Cursor Tracking and Cognitive Modeling. Management Science 0(0). source
  • Steffen Künn, Juan Palacios, Nico Pestel (2023) Indoor Air Quality and Strategic Decision Making. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4643
  • Fadong Chen, Zhi Zhu, Qiang Shen, Ian Krajbich, Todd A. Hare (2023) Intrachoice Dynamics Shape Social Decisions. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4732

Team⚓︎

  • Kilcullen, Molly, et al. "Does Team Orientation Matter? A State of the Science Review, Meta‐Analysis and Multilevel Framework." Journal of Organizational Behavior. source
  • Fang, Yulin, Derrick Neufeld, and Xiaojie Zhang. "Knowledge coordination via digital artefacts in highly dispersed teams." Information Systems Journal (2021). source
  • Dennis, Alexander S., Jordan B. Barlow, and Alan R. Dennis. "The Power of Introverts: Personality and Intelligence in Virtual Teams." Journal of Management Information Systems 39.1 (2022): 102-129. source
  • Kearney, E, Razinskas, S, Weiss, M, Hoegl, M. Gender Diversity and Team Performance Under Time Pressure: The Role of Team Withdrawal and Information Elaboration. J Organ Behav. 2022. source
  • Lorens A. Imhof, Matthias Kräkel (2022) Team Diversity and Incentives. Management Science 0(0). source
  • Tat Y. Chan, Yijun Chen, Chunhua Wu (2022) Collaborate to Compete: An Empirical Matching Game Under Incomplete Information in Rank-Order Tournaments. Marketing Science 0(0). source
  • Mullins, Jeffrey K. and Sabherwal, Rajiv. 2022. "Just Enough Information? The Contingent Curvilinear Effect of Information Volume on Decision Performance in IS-Enabled Teams," MIS Quarterly, (46: 4) pp.2197-2228. source

Sharing Economy⚓︎

  • John Tripp, D. Harrison McKnight & Nancy Lankton (2022) What most influences consumers’ intention to use? different motivation and trust stories for uber, airbnb, and taskrabbit, European Journal of Information Systems source
  • Lee, Kyunghee; Jin, Qianran (Jenny); Animesh, Animesh; and Ramaprasad, Jui. 2022. "Impact of Ride-Hailing Services on Transportation Mode Choices: Evidence from Traffic and Transit Ridership," MIS Quarterly, (46: 4) pp.1875-1900. source
  • Hyuck David Chung, Yue Maggie Zhou, Sendil Ethiraj (2023) Platform Governance in the Presence of Within-Complementor Interdependencies: Evidence from the Rideshare Industry. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4706
  • Yingjie Zhang, Beibei Li, Sean Qian (2023) Ridesharing and Digital Resilience for Urban Anomalies: Evidence from the New York City Taxi Market. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1212

IS Use⚓︎

  • Weinert, Christoph, et al. "Repeated IT Interruption: Habituation and Sensitization of User Responses." Journal of Management Information Systems 39.1 (2022): 187-217. source
  • Park, Eun Hee, et al. "Why do Family Members Reject AI in Health Care? Competing Effects of Emotions." Journal of Management Information Systems 39.3 (2022): 765-792. source

Auction⚓︎

  • Yuexin Li, Xiaoyin Ma, Luc Renneboog (2022) In Art We Trust. Management Science 0(0). source

Anti-corruption⚓︎

  • Lily Fang, Josh Lerner, Chaopeng Wu, Qi Zhang (2022) Anticorruption, Government Subsidies, and Innovation: Evidence from China. Management Science 0(0). source

Visual⚓︎

  • Sgourev, S. V., Aadland, E., & Formilan, G. (2022). Relations in Aesthetic Space: How Color Enables Market Positioning. Administrative Science Quarterly, 0(0). source

Versioning⚓︎

  • Yiting Deng, Anja Lambrecht, Yongdong Liu (2022) Spillover Effects and Freemium Strategy in the Mobile App Market. Management Science 0(0). source

Loyalty Program⚓︎

  • Federico Rossi, Pradeep K. Chintagunta (2022) Consumer Loyalty Programs and Retail Prices: Evidence from Gasoline Markets. Marketing Science 0(0). source

Promotion⚓︎

  • Hmurovic, J., Lamberton, C., & Goldsmith, K. (2022). Examining the Efficacy of Time Scarcity Marketing Promotions in Online Retail. Journal of Marketing Research, 0(0). source
  • Øystein Daljord, Carl F. Mela, Jason M. T. Roos, Jim Sprigg, Song Yao (2023) The Design and Targeting of Compliance Promotions. Marketing Science 0(0). https://doi.org/10.1287/mksc.2022.1420

Customization⚓︎

  • Fuchs, M., & Schreier, M. (2023). Paying Twice for Aesthetic Customization? The Negative Effect of Uniqueness on a Product’s Resale Value. Journal of Marketing Research, 0(0). source

Blockchain⚓︎

  • Xia Chen, Qiang Cheng, Ting Luo (2023) The Economic Value of Blockchain Applications: Early Evidence from Asset-Backed Securities. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4671

Donation⚓︎

  • Waites, S. F., Farmer, A., Hasford, J., & Welden, R. (2023). Teach a Man to Fish: The Use of Autonomous Aid in Eliciting Donations. Journal of Marketing Research, 0(0). https://doi.org/10.1177/00222437221140028

Immigration⚓︎

  • Britta Glennon (2023) How Do Restrictions on High-Skilled Immigration Affect Offshoring? Evidence from the H-1B Program. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4715

Production and Collaboration⚓︎

  • Abhishek Deshmane, Victor Martínez-de-Albéniz (2023) Come Together, Right Now? An Empirical Study of Collaborations in the Music Industry. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4743

Education⚓︎

  • Mingyu Chen (2023) The Value of U.S. College Education in Global Labor Markets: Experimental Evidence from China. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4745

IT Systems⚓︎

  • Amrit Tiwana, Hani Safadi (2023) Atrophy in Aging Systems: Evidence, Dynamics, and Antidote. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1218

© License⚓︎

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Last Update On 2023-04-15.

Copyright © 2019 - 2023 Yihong Liu
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Empirical Model⚓︎

Empirical Methodology⚓︎

  • Bertrand, Marianne, Esther Duflo, and Sendhil Mullainathan. "How much should we trust differences-in-differences estimates?." The Quarterly journal of economics 119.1 (2004): 249-275. source
  • Tafti, Ali R., and Galit Shmueli. "Beyond Overall Treatment Effects: Leveraging Covariates in Randomized Experiments Guided by Causal Structure." Available at SSRN 3331772 (2019). source
  • Xu, Yiqing. "Generalized synthetic control method: Causal inference with interactive fixed effects models." Political Analysis 25.1 (2017): 57-76.source
  • Rubin, Donald B., and Richard P. Waterman. "Estimating the causal effects of marketing interventions using propensity score methodology." Statistical Science (2006): 206-222. source
  • Athey, Susan, and Stefan Wager. "Estimating treatment effects with causal forests: An application." arXiv preprint arXiv:1902.07409 (2019). source
  • Langer, Nishtha, Ram D. Gopal, and Ravi Bapna. "Onward and Upward? An Empirical Investigation of Gender and Promotions in Information Technology Services." Information Systems Research (2020). source
  • Zhang, Yingjie, et al. "Personalized mobile targeting with user engagement stages: Combining a structural hidden markov model and field experiment." Information Systems Research 30.3 (2019): 787-804. source
  • Zhong, Ning, and David A. Schweidel. "Capturing changes in social media content: a multiple latent changepoint topic model." Marketing Science (2020). source
  • Bertsimas, Dimitris, and Nathan Kallus. "From predictive to prescriptive analytics." Management Science 66.3 (2020): 1025-1044. source
  • Wang, Guihua, Jun Li, and Wallace J. Hopp. "An instrumental variable tree approach for detecting heterogeneous treatment effects in observational studies." Ross School of Business Paper (2018). source
  • Jing Peng (2022) Identification of Causal Mechanisms from Randomized Experiments: A Framework for Endogenous Mediation Analysis. Information Systems Research 0(0). source
  • Jiaxu Peng, Jungpil Hahn, Ke-Wei Huang (2022) Handling Missing Values in Information Systems Research: A Review of Methods and Assumptions. Information Systems Research 0(0). source
  • Goldfarb A, Tucker C, Wang Y. Conducting Research in Marketing with Quasi-Experiments. Journal of Marketing. 2022;86(3):1-20. source
  • Mattke, J., Maier, C., Weitzel, T., Gerow, J. E., & Thatcher, J. B. (2022). Qualitative Comparative Analysis (QCA) In Information Systems Research: Status Quo, Guidelines, and Future Directions. Communications of the Association for Information Systems, 50, pp-pp. source
  • Haschka, Rouven E. “Handling Endogenous Regressors Using Copulas: A Generalization to Linear Panel Models with Fixed Effects and Correlated Regressors.” Journal of Marketing Research, Apr. 2022. source
  • Skinner, Richard J.; Nelson, R. Ryan; and Chin, Wynne (2022) "Synthesizing Qualitative Evidence: A Roadmap for Information Systems Research," Journal of the Association for Information Systems, 23(3), 639-677. source
  • Jiang, Dan; Jiang, Lianlian (Dorothy); Jackie, Jackie Jr.; Grover, Varun; and Sun, Heshan. 2022. "Everything Old Can Be New Again: Reinvigorating Theory Borrowing for the Digital Age," MIS Quarterly, (46: 4) pp.1833-1850. source
  • Golder, Peter N., et al. "Learning from data: An empirics-first approach to relevant knowledge generation." Journal of Marketing (2022). source
  • Fink, Lior (2022) "Why and How Online Experiments Can Benefit Information Systems Research," Journal of the Association for Information Systems, 23(6), 1333-1346. source
  • Morris, Shad, et al. "Theorizing From Emerging Markets: Challenges, Opportunities, and Publishing Advice." Academy of Management Review 48.1 (2023): 1-10. source

Causality and Machine Learning⚓︎

  • Pearl, Judea. "Causal inference in statistics: An overview." Statistics surveys 3 (2009): 96-146. source
  • Schölkopf, Bernhard. "Causality for machine learning." arXiv preprint arXiv:1911.10500 (2019). source
  • Guo, Ruocheng, et al. "A survey of learning causality with data: Problems and methods." ACM Computing Surveys (CSUR) 53.4 (2020): 1-37. source
  • Yao, Liuyi, et al. "A survey on causal inference." arXiv preprint arXiv:2002.02770 (2020). source
  • Schnabel, Tobias, et al. "Recommendations as treatments: Debiasing learning and evaluation." international conference on machine learning. PMLR, 2016. source
  • Bonner, Stephen, and Flavian Vasile. "Causal embeddings for recommendation." Proceedings of the 12th ACM conference on recommender systems. 2018. source
  • Wang, Yixin, et al. "Causal Inference for Recommender Systems." Fourteenth ACM Conference on Recommender Systems. 2020. source
  • Chen, Jiawei, et al. "AutoDebias: Learning to Debias for Recommendation." arXiv preprint arXiv:2105.04170 (2021). source
  • Brett R. Gordon, Robert Moakler, Florian Zettelmeyer (2022) Close Enough? A Large-Scale Exploration of Non-Experimental Approaches to Advertising Measurement. Marketing Science 0(0). source
  • Nicholas P. Danks, Soumya Ray, Galit Shmueli (2023) The Composite Overfit Analysis Framework: Assessing the Out-of-Sample Generalizability of Construct-Based Models Using Predictive Deviance, Deviance Trees, and Unstable Paths. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4705
  • Microsoft Research hosts its causality research at Causality and Machine Learning

Theories⚓︎

  • Gregor, S. (2006). The nature of theory in information systems. MIS quarterly, 611-642. https://doi.org/10.2307/25148742
  • Fink, L. (2021). The Philosopher's Corner: The Role of Theory in Information Systems Research. ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 52(3), 96-103. https://dl.acm.org/doi/10.1145/3481629.3481636
  • Andrade, A, et al (2023) The importance of theory at the Information Systems Journal. Information Systems Journal, editorial. https://doi.org/10.1111/isj.12437

Waiting Cost⚓︎

  • Osuna, Edgar Elias. "The psychological cost of waiting." Journal of Mathematical Psychology 29.1 (1985): 82-105. source

Information Systems Continuance⚓︎

  • Bhattacherjee, Anol. "Understanding information systems continuance: An expectation-confirmation model." MIS quarterly (2001): 351-370. source
  • Soliman, Wael, and Virpi Kristiina Tuunainen. "A tale of two frames: Exploring the role of framing in the use discontinuance of volitionally adopted technology." Information Systems Journal (2021). source
  • Lin, Julian; Yin, Jiamin; Wei, Kwok Kee; Chan, Hock Chuan; and Teo, Hock Hai. 2022. "Comparing Competing Systems: An Extension of the Information Systems Continuance Model," MIS Quarterly, (46: 4) pp.1851-1874. source
  • Lin, Julian; Yin, Jiamin; Wei, Kwok Kee; Chan, Hock Chuan; and Teo, Hock Hai. 2022. "Comparing Competing Systems: An Extension of the Information Systems Continuance Model," MIS Quarterly, (46: 4) pp.1851-1874. source

Expectation-Confirmation Theory⚓︎

  • Oliver, Richard L. "Effect of expectation and disconfirmation on postexposure product evaluations: An alternative interpretation." Journal of applied psychology 62.4 (1977): 480. source
  • Oliver, Richard L. "A cognitive model of the antecedents and consequences of satisfaction decisions." Journal of marketing research 17.4 (1980): 460-469. source

Theory of Acceptance⚓︎

  • Davis, Fred D. "Perceived usefulness, perceived ease of use, and user acceptance of information technology." MIS quarterly (1989): 319-340. source
  • Davis, Fred D., Richard P. Bagozzi, and Paul R. Warshaw. "User acceptance of computer technology: A comparison of two theoretical models." Management science 35.8 (1989): 982-1003. source
  • Taylor, Shirley, and Peter A. Todd. "Understanding information technology usage: A test of competing models." Information systems research 6.2 (1995): 144-176. source
  • Venkatesh, Viswanath, and Fred D. Davis. "A theoretical extension of the technology acceptance model: Four longitudinal field studies." Management science 46.2 (2000): 186-204. source
  • Venkatesh, Viswanath, et al. "User acceptance of information technology: Toward a unified view." MIS quarterly (2003): 425-478. source
  • Dwivedi, Yogesh K., et al. "A meta-analysis based modified unified theory of acceptance and use of technology (meta-UTAUT): a review of emerging literature." Current opinion in psychology 36 (2020): 13-18. source
  • Blut, Markus, et al. "Meta-Analysis of the Unified Theory of Acceptance and Use of Technology (UTAUT): Challenging its Validity and Charting a Research Agenda in the Red Ocean," Journal of the Association for Information Systems (2022), 23(1), 13-95. source
  • Christian Maier, Sven Laumer, Jason Bennett Thatcher, Jakob Wirth, Tim Weitzel (2022) Trial-Period Technostress: A Conceptual Definition and Mixed-Methods Investigation. Information Systems Research 0(0). source

Theory of Planned Behavior⚓︎

Intrinsic Motivation⚓︎

  • Deci, Edward L., and Richard M. Ryan. Intrinsic motivation and self-determination in human behavior. Springer Science & Business Media, 2013. source

Self-Determination Theory⚓︎

  • Deci, Edward L., and Richard M. Ryan. "The" what" and" why" of goal pursuits: Human needs and the self-determination of behavior." Psychological inquiry 11.4 (2000): 227-268. source
  • Ryan, Richard M., and Edward L. Deci. "Intrinsic and extrinsic motivations: Classic definitions and new directions." Contemporary educational psychology 25.1 (2000): 54-67. source
  • Ryan, Richard M., and Edward L. Deci. "Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being." American psychologist 55.1 (2000): 68. source
  • Deci, Edward L., and Richard M. Ryan. Intrinsic motivation and self-determination in human behavior. Springer Science & Business Media, 2013. source

Belief, Attitude, Intention & Behavior⚓︎

  • Fishbein, Martin, and Icek Ajzen. "Belief, attitude, intention, and behavior: An introduction to theory and research." Philosophy and Rhetoric 10.2 (1977). source

Uses and Gratifications⚓︎

  • Ruggiero, Thomas E. "Uses and gratifications theory in the 21st century." Mass communication & society 3.1 (2000): 3-37. source
  • Weiyan, L. I. U. "A historical overview of uses and gratifications theory." Cross-Cultural Communication 11.9 (2015): 71-78. source

Time Allocation⚓︎

  • Becker, Gary S. "A Theory of the Allocation of Time." The economic journal 75.299 (1965): 493-517. source

Social Norms⚓︎

  • Deutsch, Morton, and Harold B. Gerard. "A study of normative and informational social influences upon individual judgment." The journal of abnormal and social psychology 51.3 (1955): 629. source
  • Gibbs, Jack P. "Norms: The problem of definition and classification." American Journal of Sociology 70.5 (1965): 586-594. source
  • Lapinski, Maria Knight, and Rajiv N. Rimal. "An explication of social norms." Communication theory 15.2 (2005): 127-147. source
  • Young, H. Peyton. "The evolution of social norms." economics 7.1 (2015): 359-387. source
  • Legros, Sophie, and Beniamino Cislaghi. "Mapping the social-norms literature: An overview of reviews." Perspectives on Psychological Science 15.1 (2020): 62-80. source
  • Horne, Christine, and Stefanie Mollborn. "Norms: An integrated framework." Annual Review of Sociology 46 (2020): 467-487. source
  • Eugen Dimant (2023) Hate Trumps Love: The Impact of Political Polarization on Social Preferences. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4701

Targeting with Mobile Coupons⚓︎

  • Ghose, Anindya, et al. "Seizing the commuting moment: Contextual targeting based on mobile transportation apps." Information Systems Research 30.1 (2019): 154-174. source
  • Andrews, Michelle, et al. "Mobile ad effectiveness: Hyper-contextual targeting with crowdedness." Marketing Science 35.2 (2016): 218-233. source

Multichannel Advertising and Retailing⚓︎

  • Ghose, Anindya, and Vilma Todri. "Towards a digital attribution model: Measuring the impact of display advertising on online consumer behavior." Available at SSRN 2672090 (2015). source
  • Kumar, Anuj, Amit Mehra, and Subodha Kumar. "Why do stores drive online sales? Evidence of underlying mechanisms from a multichannel retailer." Information Systems Research 30.1 (2019): 319-338. source
  • Che, Tong, et al. "Online prejudice and barriers to digital innovation: Empirical investigations of Chinese consumers." Information Systems Journal (2021). source
  • Wei Chen, Zaiyan Wei, Karen Xie (2022) The Battle for Homes: How Does Home Sharing Disrupt Local Residential Markets?. Management Science 0(0). source
  • Scott K. Shriver, Bryan Bollinger (2022) Demand Expansion and Cannibalization Effects from Retail Store Entry: A Structural Analysis of Multichannel Demand. Management Science 0(0). source

Advertising and Recommendations⚓︎

  • Kumar, Anuj, and Yinliang Tan. "The demand effects of joint product advertising in online videos." Management Science 61.8 (2015): 1921-1937. source
  • Kumar, Anuj, and Kartik Hosanagar. "Measuring the value of recommendation links on product demand." Information Systems Research 30.3 (2019): 819-838. source
  • Matthew McGranaghan, Jura Liaukonyte, Kenneth C. Wilbur (2022) How Viewer Tuning, Presence, and Attention Respond to Ad Content and Predict Brand Search Lift. Marketing Science 0(0). source
  • Adamopoulos, Panagiotis, Anindya Ghose, and Alexander Tuzhilin. "Heterogeneous demand effects of recommendation strategies in a mobile application: Evidence from econometric models and machine-learning instruments." MIS Quarterly (2022). source
  • Tesary Lin, Sanjog Misra (2022) Frontiers: The Identity Fragmentation Bias. Marketing Science 0(0). source
  • Ada, Sıla, Nadia Abou Nabout, and Elea McDonnell Feit. "EXPRESS: Context Information can Increase Revenue in Online Display Advertising Auctions: Evidence from a Policy Change." Journal of Marketing Research (2021). source
  • Rafieian, Omid, and Hema Yoganarasimhan. “Variety Effects in Mobile Advertising.” Journal of Marketing Research, Apr. 2022. source
  • Ghosh Dastidar, A., Sunder, S., & Shah, D. (2022). Societal Spillovers of TV Advertising: Social Distancing During a Public Health Crisis. Journal of Marketing, 0(0). source
  • Weijia Dai, Hyunjin Kim, Michael Luca (2023) Frontiers: Which Firms Gain from Digital Advertising? Evidence from a Field Experiment. Marketing Science 0(0). https://doi.org/10.1287/mksc.2023.1436

Technology⚓︎

Educational Technology⚓︎

  • Kumar, Anuj, and Amit Mehra. "Remedying Education with Personalized Homework: Evidence from a Randomized Field Experiment in India." Available at SSRN 2756059 (2018). source
  • Qiang Gao, Mingfeng Lin, D. J. Wu (2021) Education Crowdfunding and Student Performance: An Empirical Study. Information Systems Research 32(1):53-71. source
  • Samantha M. Keppler, Jun Li, Di (Andrew) Wu (2022) Crowdfunding the Front Lines: An Empirical Study of Teacher-Driven School Improvement. Management Science 0(0). source

Green Technology⚓︎

  • Saldanha, Terence J. V.; Mithas, Sunil; Khuntia, Jiban; Whitaker, Jonathan; and Melville, Nigel P.. 2022. "How Green Information Technology Standards and Strategies Influence Performance: Role of Environment, Cost, and Dual Focus," MIS Quarterly, (46: 4) pp.2367-2386. source
  • Zhiling Guo, Jin Li, Ram Ramesh (2023) Green Data Analytics of Supercomputing from Massive Sensor Networks: Does Workload Distribution Matter?. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1208

Facial Recognition⚓︎

  • Jia Gao, Ying Rong, Xin Tian, Yuliang Yao (2023) Improving Convenience or Saving Face? An Empirical Analysis of the Use of Facial Recognition Payment Technology in Retail. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1205

Voice⚓︎

  • Melzner, J., Bonezzi, A., & Meyvis, T. (2023). Information Disclosure in the Era of Voice Technology. Journal of Marketing, 0(0). https://doi.org/10.1177/00222429221138286

Healthcare⚓︎

  • Elina H. Hwang, Xitong Guo, Yong Tan, Yuanyuan Dang (2022) Delivering Healthcare Through Teleconsultations: Implications for Offline Healthcare Disparity. Information Systems Research 0(0). source
  • Ginger Zhe Jin, Ajin Lee, Susan Feng Lu (2022) Patient Routing to Skilled Nursing Facilities: The Consequences of the Medicare Reimbursement Rule. Management Science 0(0). source
  • Ghose, Anindya, et al. "Empowering patients using smart mobile health platforms: Evidence from a randomized field experiment." MIS Quarterly (2022). source
  • Clary, G., Dick, G., Akbulut, A. Y., & Van Slyke, C. (2022). The After Times: College Students’ Desire to Continue with Distance Learning Post Pandemic. Communications of the Association for Information Systems, 50, pp-pp. source
  • Gorkem Turgut Ozer, Brad N. Greenwood, Anandasivam Gopal (2022) Digital Multisided Platforms and Women’s Health: An Empirical Analysis of Peer-to-Peer Lending and Abortion Rates. Information Systems Research 0(0). source
  • Shelly Rathee, Kritika Narula, Arul Mishra, Himanshu Mishra (2022) Alphanumeric vs. Numeric Token Systems and the Healthcare Experience: Field Evidence from Healthcare Delivery in India. Management Science 0(0). source
  • Sykes, Tracy Ann, and Ruba Aljafari. "We Are All in This Together, or Are We? Job Strain and Coping in the Context of an E-Healthcare System Implementation." Journal of Management Information Systems 39.4 (2022): 1215-1247. https://doi.org/10.1080/07421222.2022.2127450
  • Temidayo Adepoju, Anita L. Carson, Helen S. Jin, Christopher S. Manasseh (2023) Hospital Boarding Crises: The Impact of Urgent vs. Prevention Responses on Length of Stay. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4724
  • Sezgin Ayabakan, Indranil R. Bardhan, Zhiqiang (Eric) Zheng (2023) Impact of Telehealth and Process Virtualization on Healthcare Utilization. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1220
  • Thiebes, S., Gao, F., Briggs, R. O., Schmidt-Kraepelin, M., & Sunyaev, A. (2023). Design Concerns for Multiorganizational, Multistakeholder Collaboration: A Study in the Healthcare Industry. Journal of management information systems, 1. https://doi.org/10.1080/07421222.2023.2172771

Pandemic⚓︎

  • Marta Serra-Garcia, Nora Szech (2022) Incentives and Defaults Can Increase COVID-19 Vaccine Intentions and Test Demand. Management Science 0(0). source
  • Joseph R. Buckman, Idris Adjerid, Catherine Tucker (2022) Privacy Regulation and Barriers to Public Health. Management Science 0(0). source
  • Jean-Philippe Bonardi, Quentin Gallea, Dimitrija Kalanoski, Rafael Lalive (2023) Managing Pandemics: How to Contain COVID-19 Through Internal and External Lockdowns and Their Release. Management Science 0(0). https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2022.4652

Applications of Artificial Intelligence⚓︎

  • Wang, Quan, Beibei Li, and Param Vir Singh. "Copycats vs. original mobile apps: A machine learning copycat-detection method and empirical analysis." Information Systems Research 29.2 (2018): 273-291. source
  • Burtch, Gordon, Anindya Ghose, and Sunil Wattal. "The hidden cost of accommodating crowdfunder privacy preferences: A randomized field experiment." Management Science 61.5 (2015): 949-962. source
  • Vítor Albiero, and Kevin W. Bowyer. "Is Face Recognition Sexist? No, Gendered Hairstyles and Biology Are" BMVC 2020. source
  • Garvey, Aaron M., et al. “Bad News? Send an AI. Good News? Send a Human.” Journal of Marketing, Feb. 2022. source
  • Martin Reisenbichler, Thomas Reutterer, David A. Schweidel, Daniel Dan (2022) Frontiers: Supporting Content Marketing with Natural Language Generation. Marketing Science 0(0). source
  • Andreas Barth, Sasan Mansouri, Fabian Wöbbeking, (2022) “Let Me Get Back to You”—A Machine Learning Approach to Measuring NonAnswers. Management Science 0(0). source

Software⚓︎

Piracy⚓︎

  • Martin Eisend. "Explaining Digital Piracy: A Meta-Analysis." Information Systems Research 30.2 (2019): 636-664. source
  • Christian Peukert, Stefan Bechtold, Michail Batikas, Tobias Kretschmer (2022) Regulatory Spillovers and Data Governance: Evidence from the GDPR. Marketing Science 0(0). source

Cybersecurity⚓︎

  • Kolini, F., & Janczewski, L. J. (2022). Exploring Incentives and Challenges for Cybersecurity Intelligence Sharing (CIS) across Organizations: A Systematic Review. Communications of the Association for Information Systems, 50, pp-pp. source
  • D'Arcy, John and Basoglu, Asli (2022) "The Influences of Public and Institutional Pressure on Firms’ Cybersecurity Disclosures," Journal of the Association for Information Systems, 23(3), 779-805. source
  • A. J. Burns, Tom L. Roberts, Clay Posey, Paul Benjamin Lowry, Bryan Fuller (2022) Going Beyond Deterrence: A Middle-Range Theory of Motives and Controls for Insider Computer Abuse. Information Systems Research 0(0). source
  • Nikkhah, Hamid Reza and Grover, Varun. 2022. "An Empirical Investigation of Company Response to Data Breaches," MIS Quarterly, (46: 4) pp.2163-2196. source

Electronic Participation⚓︎

  • Yo, Y., & Xu, P. (2022). The Power of Electronic Channels and Electronic Political Efficacy: Electronic Participation Discourse. Communications of the Association for Information Systems, 50, pp-pp. source

Productivity⚓︎

  • Peng Huang, Marco Ceccagnoli, Chris Forman, D.J. Wu (2022) IT Knowledge Spillovers, Absorptive Capacity, and Productivity: Evidence from Enterprise Software. Information Systems Research 0(0). source

Software Development⚓︎

  • Gregory Vial (2023) A Complex Adaptive Systems Perspective of Software Reuse in the Digital Age: An Agenda for IS Research. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1200

Algorithm⚓︎

Impact⚓︎

  • Athey, Susan. "The impact of machine learning on economics." The economics of artificial intelligence: An agenda. University of Chicago Press, 2018. 507-547. pdf

Bias⚓︎

  • Lambrecht, Anja, and Catherine Tucker. "Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads." Management Science 65.7 (2019): 2966-2981. source

Aversion⚓︎

  • Dietvorst, Berkeley J., Joseph P. Simmons, and Cade Massey. "Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them." Management Science 64.3 (2018): 1155-1170. source
  • Germann, Maximilian, and Christoph Merkle. "Algorithm Aversion in Financial Investing." Available at SSRN 3364850 (2019). source

Human & Algorithm⚓︎

  • Kleinberg, Jon, et al. "Human decisions and machine predictions." The quarterly journal of economics 133.1 (2018): 237-293. source
  • Liwei Chen, J. J. Po-An Hsieh, Arun Rai (2022) How Does Intelligent System Knowledge Empowerment Yield Payoffs? Uncovering the Adaptation Mechanisms and Contingency Role of Work Experience. Information Systems Research 0(0). source
  • Tarafdar, Monideepa, Xinru Page, and Marco Marabelli. "Algorithms as co‐workers: Human algorithm role interactions in algorithmic work." Information Systems Journal. source
  • Chen, Yang, et al. "Does Techno-invasion Lead to Employees’ Deviant Behaviors?." Journal of Management Information Systems 39.2 (2022): 454-482. source
  • You, Sangseok, Cathy Liu Yang, and Xitong Li. "Algorithmic versus Human Advice: Does Presenting Prediction Performance Matter for Algorithm Appreciation?." Journal of Management Information Systems 39.2 (2022): 336-365. source
  • Ghasemaghaei, Maryam, and Ofir Turel. "Why Do Data Analysts Take IT-Mediated Shortcuts? An Ego-Depletion Perspective." Journal of Management Information Systems 39.2 (2022): 483-512. source
  • Elizabeth Han, Dezhi Yin, Han Zhang (2022) Bots with Feelings: Should AI Agents Express Positive Emotion in Customer Service?. Information Systems Research 0(0). source
  • Mikhail Lysyakov , Siva Viswanathan (2022) Threatened by AI: Analyzing Users’ Responses to the Introduction of AI in a Crowd-Sourcing Platform. Information Systems Research 0(0). source
  • Nasim Mousavi, Panagiotis Adamopoulos, Jesse Bockstedt (2023) The Decoy Effect and Recommendation Systems. Information Systems Research 0(0). https://doi.org/10.1287/isre.2022.1197
  • Callen Anthony, Beth A. Bechky, Anne-Laure Fayard (2023) “Collaborating” with AI: Taking a System View to Explore the Future of Work. Organization Science 0(0). https://doi.org/10.1287/orsc.2022.1651
  • Chandra, Shalini, Anuragini Shirish, and Shirish C. Srivastava. "To Be or Not to Be… Human? Theorizing the Role of Human-Like Competencies in Conversational Artificial Intelligence Agents." Journal of Management Information Systems 39.4 (2022): 969-1005. https://doi.org/10.1080/07421222.2022.2127441
  • Tarafdar, Monideepa, Xinru Page, and Marco Marabelli. "Algorithms as co‐workers: Human algorithm role interactions in algorithmic work." Information Systems Journal (2022). https://doi.org/10.1111/isj.12389
  • Kevin Bauer, Moritz von Zahn, Oliver Hinz (2023) Expl(AI)ned: The Impact of Explainable Artificial Intelligence on Users’ Information Processing. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1199
  • Parker, Sara, and Derek Ruths. "Is hate speech detection the solution the world wants?." Proceedings of the National Academy of Sciences 120.10 (2023): e2209384120. https://doi.org/10.1073/pnas.2209384120
  • Chandra, Shalini, Anuragini Shirish, and Shirish C. Srivastava. "To Be or Not to Be… Human? Theorizing the Role of Human-Like Competencies in Conversational Artificial Intelligence Agents." Journal of Management Information Systems 39.4 (2022): 969-1005. https://doi.org/10.1080/07421222.2022.2127441
  • Boyacı, Tamer, Caner Canyakmaz, and Francis de Véricourt. "Human and Machine: The Impact of Machine Input on Decision Making Under Cognitive Limitations." Management Science (2023). https://doi.org/10.1287/mnsc.2023.4744
  • Dolata, M., Katsiuba, D., Wellnhammer, N., & Schwabe, G. (2023). Learning with Digital Agents: An Analysis based on the Activity Theory. Journal of Management Information Systems, 40(1), 56-95. https://doi.org/10.1080/07421222.2023.2172775

Gender⚓︎

  • Lin, Chen, et al. "Do "Little Emperors” Get More Than “Little Empresses"? Boy-Girl Gender Discrimination as Evidenced by Consumption Behavior of Chinese Households." Marketing Science (2021). source
  • Helena Fornwagner, Monika Pompeo, Nina Serdarevic (2022) Choosing Competition on Behalf of Someone Else. Management Science 0(0). source
  • Emilio J. Castilla, Hye Jin Rho (2023) The Gendering of Job Postings in the Online Recruitment Process. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4674
  • Eliot L. Sherman, Raina Brands, Gillian Ku (2023) Dropping Anchor: A Field Experiment Assessing a Salary History Ban with Archival Replication. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4658
  • Zhiyan Wu, Lucia Naldi, Karl Wennberg, Timur Uman (2023) Learning from Their Daughters: Family Exposure to Gender Disparity and Female Representation in Male-Led Ventures. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4727

Privacy⚓︎

  • Godinho de Matos, Miguel, and Idris Adjerid. "Consumer consent and firm targeting after GDPR: The case of a large telecom provider." Management Science (2021). source
  • Heng Xu, Nan Zhang (2022) From Contextualizing to Context Theorizing: Assessing Context Effects in Privacy Research. Management Science 0(0). source
  • Karwatzki, Sabrina et al. The multidimensional nature of privacy risks: Conceptualisation, measurement and implications for digital services. Information Systems Journal (2022). source
  • Tesary Lin (2022) Valuing Intrinsic and Instrumental Preferences for Privacy. Marketing Science 0(0). source
  • Tawfiq Alashoor, Mark Keil, H. Jeff Smith, Allen R. McConnell (2022) Too Tired and in Too Good of a Mood to Worry About Privacy: Explaining the Privacy Paradox Through the Lens of Effort Level in Information Processing. Information Systems Research 0(0). source
  • Ram D. Gopal, Hooman Hidaji, Sule Nur Kutlu, Raymond A. Patterson, Niam Yaraghi (2023) Law, Economics, and Privacy: Implications of Government Policies on Website and Third-Party Information Sharing. Information Systems Research 0(0). https://doi.org/10.1287/isre.2022.1178
  • Garrett A. Johnson, Scott K. Shriver, Samuel G. Goldberg (2023) Privacy and Market Concentration: Intended and Unintended Consequences of the GDPR. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4709

Online Platforms⚓︎

Subscription Models⚓︎

  • Oestreicher-Singer, Gal, and Lior Zalmanson. "Content or community? A digital business strategy for content providers in the social age." MIS quarterly (2013): 591-616. source
  • Bapna, Ravi, and Akhmed Umyarov. "Do your online friends make you pay? A randomized field experiment on peer influence in online social networks." Management Science 61.8 (2015): 1902-1920. source
  • Hongfei Li, Jing Peng, Xinxin Li, Jan Stallaert (2022) When More Can Be Less: The Effect of Add-On Insurance on the Consumption of Professional Services. Information Systems Research 0(0). source

Digital Content & User-Generated Content⚓︎

  • Ye, Hua, et al. "Monetization of Digital Content: Drivers of Revenue on Q&A Platforms." Journal of Management Information Systems 38.2 (2021): 457-483. source
  • Zhiyu Zeng, Hengchen Dai, Dennis J. Zhang, Heng Zhang, Renyu Zhang, Zhiwei Xu, Zuo-Jun Max Shen (2022) The Impact of Social Nudges on User-Generated Content for Social Network Platforms. Management Science 0(0). source
  • Lu, S., Dinner, I., & Grewal, R. (2023). The Ripple Effect of Firm-Generated Content on New Movie Releases. Journal of Marketing Research, 0(0). https://doi.org/10.1177/00222437221143066

Online Reviews⚓︎

  • Chen, Yan, et al. "Social comparisons and contributions to online communities: A field experiment on movielens." American Economic Review 100.4 (2010): 1358-98. source
  • Gordon Burtch, Yili Hong, Ravi Bapna, Vladas Griskevicius (2017) Stimulating Online Reviews by Combining Financial Incentives and Social Norms. Management Science 64(5):2065-2082. source
  • Limin Fang (2022) The Effects of Online Review Platforms on Restaurant Revenue, Consumer Learning, and Welfare. Management Science 0(0). source
  • Jinghui (Jove) Hou, Xiao Ma (2022) Space Norms for Constructing Quality Reviews on Online Consumer Review Sites. Information Systems Research 0(0). source
  • Sherry He, Brett Hollenbeck, Davide Proserpio (2022) The Market for Fake Reviews. Marketing Science 0(0). source
  • Honglin Deng, Weiquan Wang, Siyuan Li, and Kai H. Lim. "Can Positive Online Social Cues Always Reduce User Avoidance of Sponsored Search Results?." MIS Quarterly (2021). source
  • Mengxia Zhang, Lan Luo (2022) Can Consumer-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp. Management Science 0(0). source
  • Choi, HanByeol Stella, et al. "Effects of Online Crowds on Self-Disclosure Behaviors in Online Reviews: A Multidimensional Examination." Journal of Management Information Systems 39.1 (2022): 218-246. source
  • T. Ravichandran, Chaoqun Deng (2022) Effects of Managerial Response to Negative Reviews on Future Review Valence and Complaints. Information Systems Research 0(0). source
  • Jung, M., Ryu, S., Han, S. P., & Cho, D. (2023). Ask for Reviews at the Right Time: Evidence from Two Field Experiments. Journal of Marketing, 0(0). https://doi.org/10.1177/00222429221143329
  • Chen, Y., & Lee, S. (2023). User-Generated Physician Ratings and Their Effects on Patients’ Physician Choices: Evidence from Yelp. Journal of Marketing, 0(0). https://doi.org/10.1177/00222429221146511
  • Uttara Ananthakrishnan, Davide Proserpio, Siddhartha Sharma (2023) I Hear You: Does Quality Improve with Customer Voice?. Marketing Science 0(0). https://doi.org/10.1287/mksc.2023.1437
  • Andrey Fradkin, David Holtz (2023) Do Incentives to Review Help the Market? Evidence from a Field Experiment on Airbnb. Marketing Science 0(0). https://doi.org/10.1287/mksc.2023.1439

Digital Beauty Filter⚓︎

Platform Growth, Merge and Acquisition⚓︎

  • Chiara Farronato, Jessica Fong, Andrey Fradkin (2023) Dog Eat Dog: Balancing Network Effects and Differentiation in a Digital Platform Merger. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4675

Social Media⚓︎

  • Xu, Haifeng, et al (2022) "Why Are People Addicted to SNS? Understanding the Role of SNS Characteristics in the Formation of SNS Addiction," Journal of the Association for Information Systems, 23(3), 806-837. source
  • Peng, Jing, Juheng Zhang, and Ram Gopal. "The Good, the Bad, and the Social Media: Financial Implications of Social Media Reactions to Firm-Related News." Journal of Management Information Systems 39.3 (2022): 706-732. source

Interactions⚓︎

  • Matook, Sabine, Alan R. Dennis, and Yazhu Maggie Wang. "User comments in social media firestorms: A mixed-method study of purpose, tone, and motivation." Journal of Management Information Systems 39.3 (2022): 673-705. source
  • Lu, Yingda; Wu, Junjie; Tan, Yong; and Chen, Jian. 2022. "Microblogging Replies and Opinion Polarization: A Natural Experiment," MIS Quarterly, (46: 4) pp.1901-1936. source
  • Yun Young Hur, Fujie Jin, Xitong Li, Yuan Cheng, Yu Jeffrey Hu (2022) Does Social Influence Change with Other Information Sources? A Large-Scale Randomized Experiment in Medical Crowdfunding. Information Systems Research 0(0). source
  • Wakefield, R. L., & Wakefield, K. (2022). The antecedents and consequences of intergroup affective polarisation on social media. Information Systems Journal, 1– 29. source
  • Miller, Stacy, et al. "Integrating truth bias and elaboration likelihood to understand how political polarisation impacts disinformation engagement on social media." Information Systems Journal (2022). source
  • Wang, Lin, Chong Wang, and Xinyan Yao. "Befriended to polarise? The impact of friend identity on review polarisation—A quasi‐experiment." Information Systems Journal. https://doi.org/10.1111/isj.12425

Fake News on Social Media⚓︎

  • Wang, Shuting, Min-Seok Pang, and Paul A. Pavlou. "Cure or Poison? Identity Verification and the Posting of Fake News on Social Media." Journal of Management Information Systems 38.4 (2021): 1011-1038. source
  • Horner, Christy Galletta, et al. "Emotions: The Unexplored Fuel of Fake News on Social Media." Journal of Management Information Systems 38.4 (2021): 1039-1066. source
  • Deng, Bingjie, and Michael Chau. "The Effect of the Expressed Anger and Sadness on Online News Believability." Journal of Management Information Systems 38.4 (2021): 959-988. source
  • Turel, Ofir, and Babajide Osatuyi. "Biased Credibility and Sharing of Fake News on Social Media: Considering Peer Context and Self-Objectivity State." Journal of Management Information Systems 38.4 (2021): 931-958. source
  • Ng, Ka Chung, Jie Tang, and Dongwon Lee. "The Effect of Platform Intervention Policies on Fake News Dissemination and Survival: An Empirical Examination." Journal of Management Information Systems 38.4 (2021): 898-930. source
  • George, Jordana, Natalie Gerhart, and Russell Torres. "Uncovering the Truth about Fake News: A Research Model Grounded in Multi-Disciplinary Literature." Journal of Management Information Systems 38.4 (2021): 1067-1094. source
  • Gimpel, Henner, et al. "The effectiveness of social norms in fighting fake news on social media." Journal of Management Information Systems 38.1 (2021): 196-221. source
  • Mohamed Mostagir, Asuman Ozdaglar, James Siderius (2022) When Is Society Susceptible to Manipulation?. Management Science 0(0). source
  • Jackie London Jr., Siyuan Li, Heshan Sun (2022) Seems Legit: An Investigation of the Assessing and Sharing of Unverifiable Messages on Online Social Networks. Information Systems Research 0(0). source
  • Mohamed Mostagir, James Siderius (2022) Learning in a Post-Truth World. Management Science 0(0). source
  • Wang, Shuting (Ada); Pang, Min-Seok; and Pavlou, Paul A.. 2022. "Seeing Is Believing? How Including a Video in Fake News Influences Users’ Reporting of Fake News to Social Media Platforms," MIS Quarterly, (46: 3) pp.1323-1354. source
  • Gizem Ceylan, Ian A. Anderson, and Wendy Wood. 2022. "Sharing of misinformation is habitual, not just lazy or biased," PNAS, (120:4) https://doi.org/10.1073/pnas.2216614120

Social Media Marketing⚓︎

  • Tingting Nian, Arun Sundararajan (2022) Social Media Marketing, Quality Signaling, and the Goldilocks Principle. Information Systems Research 0(0). source
  • Jens Foerderer, Sebastian W. Schuetz (2022) Data Breach Announcements and Stock Market Reactions: A Matter of Timing?. Management Science 0(0). source
  • Naveen Kumar, Liangfei Qiu, Subodha Kumar (2022) A Hashtag Is Worth a Thousand Words: An Empirical Investigation of Social Media Strategies in Trademarking Hashtags. Information Systems Research 0(0). source
  • Venkatesan, Srikanth, et al. "INFLUENCE IN SOCIAL MEDIA: AN INVESTIGATION OF TWEETS SPANNING THE 2011 EGYPTIAN REVOLUTION." MIS Quarterly 45.4 (2021). source
  • Alibakhshi, Reza, and Shirish C. Srivastava. "Post-Story: Influence of Introducing Story Feature on Social Media Posts." Journal of Management Information Systems 39.2 (2022): 573-601. source
  • Weiler, Michael, et al. " Social Capital Accumulation Through Social Media Networks: Evidence from a Randomized Field Experiment and Individual-Level Panel Data," Management Information Systems Quarterly, (2021). source
  • Leung, Fine F., et al. "Influencer Marketing Effectiveness." Journal of Marketing (2022) source
  • Liadeli, G., Sotgiu, F., & Verlegh, P. W. J. (2022). A Meta-Analysis of the Effects of Brands’ Owned Social Media on Social Media Engagement and Sales. Journal of Marketing, 0(0). source
  • Woolley, K., Kupor, D., & Liu, P. J. (2022). Does Company Size Shape Product Quality Inferences? Larger Companies Make Better High-Tech Products, but Smaller Companies Make Better Low-Tech Products. Journal of Marketing Research, 0(0). source

Norms And Roles⚓︎

  • Emmanuelle Vaast, Alain Pinsonneault (2022) Dealing with the Social Media Polycontextuality of Work. Information Systems Research 0(0). source
  • Verena Schoenmueller, Oded Netzer, Florian Stahl (2022) Frontiers: Polarized America: From Political Polarization to Preference Polarization. Marketing Science 0(0). source

Social Investing⚓︎

  • Jake An, Donnel Briley, Shai Danziger, Shai Levi (2022) The Impact of Social Investing on Charitable Donations. Management Science 0(0). source

Network & Graph⚓︎

  • Mariia Petryk, Michael Rivera, Siddharth Bhattacharya, Liangfei Qiu, Subodha Kumar (2022) How Network Embeddedness Affects Real-Time Performance Feedback: An Empirical Investigation. Information Systems Research 0(0). source
  • Rohit Aggarwal, Vishal Midha, Nicholas Sullivan (2023) Effect of Online Professional Network Recommendations on the Likelihood of an Interview: A Field Study. Information Systems Research 0(0). https://doi.org/10.1287/isre.2021.1053
  • Rohit Aggarwal, Vishal Midha, Nicholas Sullivan (2023) The Effect of Gender Expectations and Physical Attractiveness on Discussion of Weakness in Online Professional Recommendations. Information Systems Research 0(0). https://doi.org/10.1287/isre.2021.1032

Content Consumption & Sharing⚓︎

  • Hyelim Oh, Khim-Yong Goh, Tuan Q. Phan (2022) Are You What You Tweet? The Impact of Sentiment on Digital News Consumption and Social Media Sharing. Information Systems Research 0(0). source
  • Barnea, U., Meyer, R. J., & Nave, G. (2023). The Effects of Content Ephemerality on Information Processing. Journal of Marketing Research, 0(0). https://doi.org/10.1177/00222437221131047

News⚓︎

  • O’Riordan, S., Emerson, B., Feller, J., & Kiely, G. (2023). The Road to Open News: A Theory of Social Signaling in an Open News Production Community. Journal of Management Information Systems, 40(1), 130-162. https://doi.org/10.1080/07421222.2023.2172777

E-Commerce & Online Shopping⚓︎

  • McKnight, D. Harrison, Vivek Choudhury, and Charles Kacmar. "Developing and validating trust measures for e-commerce: An integrative typology." Information systems research 13.3 (2002): 334-359. source
  • Shang, Rong-An, Yu-Chen Chen, and Lysander Shen. "Extrinsic versus intrinsic motivations for consumers to shop on-line." Information & management 42.3 (2005): 401-413. source
  • Kim, Hee-Woong, Hock Chuan Chan, and Atreyi Kankanhalli. "What motivates people to purchase digital items on virtual community websites? The desire for online self-presentation." Information systems research 23.4 (2012): 1232-1245. source
  • Pavlou, Paul A. "Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model." International journal of electronic commerce 7.3 (2003): 101-134. source
  • Arvind K. Tripathi, Young-Jin Lee, Amit Basu (2022) Analyzing the Impact of Public Buyer–Seller Engagement During Online Auctions. Information Systems Research 0(0). source
  • Khan, A., & Krishnan, S. (2022). Ethical Behavior of Firms and B2C E-commerce Diffusion: Exploring the Mediating Roles of Customer Orientation and Innovation Capacity. Communications of the Association for Information Systems, 50, pp-pp. source
  • Iyengar R, Park Y-H, Yu Q. The Impact of Subscription Programs on Customer Purchases. Journal of Marketing Research. 2022. source
  • Yufeng Huang, Bart J. Bronnenberg (2022) Consumer Transportation Costs and the Value of E-Commerce: Evidence from the Dutch Apparel Industry. Marketing Science 0(0). source
  • Bei, Z., & Gielens, K. (2022). The One-Party Versus Third-Party Platform Conundrum: How Can Brands Thrive? Journal of Marketing, 0(0). source
  • Deng, Honglin; Wang, Weiquan; and Lim, Kai H.. 2022. "Repairing Integrity-Based Trust Violations in Ascription Disputes for Potential E-Commerce Customers," MIS Quarterly, (46: 4) pp.1983-2014. source
  • Murat Unal, Young-Hoon Park (2023) Fewer Clicks, More Purchases. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4716
  • Daniel W. Elfenbein, Raymond Fisman, Brian McManus (2023) The Impact of Socioeconomic and Cultural Differences on Online Trade. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4681
  • Yue Guan, Yong Tan, Qiang Wei, Guoqing Chen (2023) When Images Backfire: The Effect of Customer-Generated Images on Product Rating Dynamics. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1201
  • Ren, Fei; Tan, Yong; and Wan, Fei. 2023. "Know Your Firm: Managing Social Media Engagement to Improve Firm Sales Performance," MIS Quarterly, (47: 1) pp.227-262. https://aisel.aisnet.org/misq/vol47/iss1/10/
  • Geneviève Bassellier, Jui Ramaprasad (2023) All External Reference Prices Are Not the Same: How Magnitude, Source, and Fairness Shape Payment for Digital Goods. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1206
  • Ilya Morozov (2023) Measuring Benefits from New Products in Markets with Information Frictions. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4729
  • KC, R. P., Mak, V., & Ofek, E. (2023). Before or After? The Effects of Payment Decision Timing in Pay-What-You-Want Contexts. Journal of Marketing, 0(0). https://doi.org/10.1177/00222429221142234

Crowdfunding Markets⚓︎

  • Yan Xu, Jian Ni (2022) Entrepreneurial Learning and Disincentives in Crowdfunding Markets. Management Science 0(0). source
  • Kao, Ta-Wei, et al. "Deriving Execution Effectiveness of Crowdfunding Projects from the Fundraiser Network." Journal of Management Information Systems 39.1 (2022): 276-301. source
  • Rhue, Lauren and Clark, Jessica. 2022. "Who Are You and What Are You Selling? Creator-Based and Product-Based Racial Cues in Crowdfunding," MIS Quarterly, (46: 4) pp.2229-2260. source
  • Markus Weinmann, Abhay Nath Mishra, Lena Franziska Kaiser, Jan vom Brocke (2022) The Attraction Effect in Crowdfunding. Information Systems Research 0(0). source
  • Lin, Mingfeng, et al. "# Experts vs. Non-Experts in Online Crowdfunding Markets." Management Information Systems Quarterly 47.1 (2023): 97-126. https://aisel.aisnet.org/misq/vol47/iss1/6

Crowdsourcing⚓︎

  • Cao, Fang, et al. "Do Social Dominance-Based Faultlines Help or Hurt Team Performance in Crowdsourcing Tournaments?." Journal of Management Information Systems 39.1 (2022): 247-275. source
  • Deodhar, Swanand J.; Babar, Yash; and Burtch, Gordon. 2022. "The Influence of Status on Evaluations: Evidence from Online Coding Contests," MIS Quarterly, (46: 4) pp.2085-2110. source
  • Yan, Bei, and Andrea B. Hollingshead. "Motivating the Motivationally Diverse Crowd: Social Value Orientation and Reward Structure in Crowd Idea Generation." Journal of Management Information Systems 39.4 (2022): 1064-1088. https://doi.org/10.1080/07421222.2022.2127451

Social Reputation⚓︎

  • Ohanian, Roobina. "Construction and validation of a scale to measure celebrity endorsers' perceived expertise, trustworthiness, and attractiveness." Journal of advertising 19.3 (1990): 39-52. source
  • Swanand J. Deodhar, Samrat Gupta (2022) The Impact of Social Reputation Features in Innovation Tournaments: Evidence from a Natural Experiment. Information Systems Research 0(0). source
  • David Keith, Lauren Taylor, James Paine, Richard Weisbach, Anthony Dowidowicz (2022) When Funders Aren’t Customers: Reputation Management and Capability Underinvestment in Multiaudience Organizations. Organization Science 0(0). source

Waiting & Delays⚓︎

  • Taylor, Shirley. "Waiting for service: the relationship between delays and evaluations of service." Journal of marketing 58.2 (1994): 56-69. source
  • Dellaert, Benedict GC, and Barbara E. Kahn. "How tolerable is delay?: Consumers’ evaluations of internet web sites after waiting." Journal of interactive marketing 13.1 (1999): 41-54. source
  • Hoxmeier, John A., and Chris DiCesare. "System response time and user satisfaction: An experimental study of browser-based applications." (2000). source
  • Weinberg, Bruce D. "Don't keep your internet customers waiting too long at the (virtual) front door." Journal of interactive marketing 14.1 (2000): 30-39. source
  • Galletta, Dennis F., et al. "Web site delays: How tolerant are users?." Journal of the Association for Information Systems 5.1 (2004): 1. source
  • Nah, Fiona Fui-Hoon. "A study on tolerable waiting time: how long are web users willing to wait?." Behaviour & Information Technology 23.3 (2004): 153-163. source
  • Lee, Younghwa, Andrew NK Chen, and Virginia Ilie. "Can online wait be managed? The effect of filler interfaces and presentation modes on perceived waiting time online." Mis Quarterly (2012): 365-394. source

Word of Mouth And Behavior⚓︎

  • Kunst, Katrine, Torsten Ringberg, and Ravi Vatrapu. "Beyond popularity: A user perspective on observable behaviours in a digital platform." Information Systems Journal (2021). source
  • Tao Lu, May Yuan, Chong (Alex) Wang, Xiaoquan (Michael) Zhang (2022) Histogram Distortion Bias in Consumer Choices. Management Science 0(0). source
  • Sabzehzar, Amin, et al. "Putting Religious Bias in Context: How Offline and Online Contexts Shape Religious Bias in Online Prosocial Lending." Management Information Systems Quarterly 47.1 (2023): 33-62. https://aisel.aisnet.org/misq/vol47/iss1/4

Live Stream⚓︎

  • Guan, Zhengzhi, et al. "What influences the purchase of virtual gifts in live streaming in China? A cultural context‐sensitive model." Information Systems Journal (2021). source
  • Sim, Jaeung, et al. "In-Consumption Information Cues and Digital Content Demand: Evidence from a Live-Streaming Platform." Available at SSRN 3922723 (2021). source
  • Brett, Noel. "Why Do We Only Get Anime Girl Avatars? Collective White Heteronormative Avatar Design in Live Streams." Television & New Media (2022): source

Collaboration⚓︎

- Li, He, Chen Zhang, and William J. Kettinger. "DIGITAL PLATFORM ECOSYSTEM DYNAMICS: THE ROLES OF PRODUCT SCOPE, INNOVATION, AND COLLABORATIVE NETWORK CENTRALITY." MIS Quarterly 46.2 (2022). source⚓︎

Communities⚓︎

  • Li, Yang-Jun; Cheung, Christy M.K; Shen, Xiao-Liang; and Lee, Matthew K. O. (2022) "When Socialization Goes Wrong: Understanding the We-Intention to Participate in Collective Trolling in Virtual Communities," Journal of the Association for Information Systems, 23(3), 678-706. source
  • Zhou, Jiaqi, et al. "Unintended Emotional Effects of Online Health Communities: A Text Mining-Supported Empirical Study." Management Information Systems Quarterly 47.1 (2023): 195-226. https://aisel.aisnet.org/misq/vol47/iss1/9

Online Dating⚓︎

  • Ravi Bapna, Edward McFowland, III, Probal Mojumder, Jui Ramaprasad, Akhmed Umyarov (2022) So, Who Likes You? Evidence from a Randomized Field Experiment. Management Science 0(0). source

Generativity⚓︎

  • Daniel Fürstenau, Abayomi Baiyere, Kai Schewina, Matthias Schulte-Althoff, Hannes Rothe (2023) Extended Generativity Theory on Digital Platforms. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1209

Video Games⚓︎

  • Michael, David R., and Sandra L. Chen. Serious games: Games that educate, train, and inform. Muska & Lipman/Premier-Trade, 2005.
  • Le Wang, Yongqiang Sun, Xin (Robert) Luo. (2022) "Game affordance, gamer orientation, and in-game purchases: A hedonic–instrumental framework," Information Systems Journal. source
  • Le Wang, Paul Benjamin Lowry, Xin (Robert) Luo, Han Li (2022) Moving Consumers from Free to Fee in Platform-Based Markets: An Empirical Study of Multiplayer Online Battle Area Games. Information Systems Research 0(0). source

Gamification⚓︎

  • Zichermann, Gabe, and Christopher Cunningham. Gamification by design: Implementing game mechanics in web and mobile apps. " O'Reilly Media, Inc.", 2011.
  • Huotari, Kai, and Juho Hamari. "Defining gamification: a service marketing perspective." Proceeding of the 16th international academic MindTrek conference. 2012. source
  • Hamari, Juho, Jonna Koivisto, and Harri Sarsa. "Does gamification work?--a literature review of empirical studies on gamification." 2014 47th Hawaii international conference on system sciences. Ieee, 2014. source
  • Seaborn, Katie, and Deborah I. Fels. "Gamification in theory and action: A survey." International Journal of human-computer studies 74 (2015): 14-31. source
  • Koivisto, Jonna, and Juho Hamari. "The rise of motivational information systems: A review of gamification research." International Journal of Information Management 45 (2019): 191-210. source
  • Behnaz Bojd, Xiaolong Song, Yong Tan, Xiangbin Yan (2022) Gamified Challenges in Online Weight-Loss Communities. Information Systems Research 0(0). source
  • Kwak, Dong-Heon; Deng, Shuyuan; Kuem, Jungwon; and Kim, Sung S. (2022) "How to Achieve Goals in Digital Games: An Empirical Test of a Goal-Oriented Model in Pokémon GO," Journal of the Association for Information Systems, 23(2), 553-588. source
  • Alvin Chung Man Leung, Radhika Santhanam, Ron Chi-Wai Kwok, Wei Thoo Yue (2022) Could Gamification Designs Enhance Online Learning Through Personalization? Lessons from a Field Experiment. Information Systems Research 0(0). source
  • Jensen, Matthew L., et al. "Improving Phishing Reporting Using Security Gamification." Journal of Management Information Systems 39.3 (2022): 793-823. source
  • Muhammad Zia Hydari, Idris Adjerid, Aaron D. Striegel (2022) Health Wearables, Gamification, and Healthful Activity. Management Science 0(0). source

Video Game Live-streaming⚓︎

  • Li, Yi, Chongli Wang, and Jing Liu. "A systematic review of literature on user behavior in video game live streaming." International Journal of Environmental Research and Public Health 17.9 (2020): 3328. source
  • Simon Bründl, Christian Matt, Thomas Hess & Simon Engert (2022) How Synchronous Participation Affects the Willingness to Subscribe to Social Live Streaming Services: The Role of Co-Interactive Behavior on Twitch, European Journal of Information Systems source

Video Games & Mental Health⚓︎

  • Cheng, Zhi, Brad N. Greenwood, and Paul A. Pavlou. "Location-based mobile gaming and local depression trends: a study of Pokémon Go." Journal of Management Information Systems 39.1 (2022): 68-101. source

Peer Influence⚓︎

  • Jung, JaeHwuen, et al. "Words Matter! Toward a Prosocial Call-to-Action for Online Referral: Evidence from Two Field Experiments." Information Systems Research (2020). source
  • Bryan Bollinger, Kenneth Gillingham, A. Justin Kirkpatrick, Steven Sexton (2022) Visibility and Peer Influence in Durable Good Adoption. Marketing Science 0(0). source
  • Rodrigo Belo, Ting Li (2022) Social Referral Programs for Freemium Platforms. Management Science 0(0). source
  • Pyo T-H, Lee JY, Park HM. The Effects of Consumer Preference and Peer Influence on Trial of an Experience Good. Journal of Marketing Research. May 2022. source

Consumer Preference⚓︎

  • Pao-Li Chang, Tomoki Fujii, Wei Jin (2022) Good Names Beget Favors: The Impact of Country Image on Trade Flows and Welfare. Management Science 0(0). source
  • Andrew Meyer, Sean Hundtofte (2023) The Longshot Bias Is a Context Effect. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4684

Labor & Market⚓︎

Labor Supply⚓︎

  • Hai Long Duong, Junhong Chu, Dai Yao (2022) Taxi Drivers’ Response to Cancellations and No-Shows: New Evidence for Reference-Dependent Preferences. Management Science 0(0). source
  • Mithas, Sunil; Chen, Yanzhen; Liu, Che-Wei; and Han, Kunsoo. 2022. "Are Foreign and Domestic Information Technology Professionals Complements or Substitutes?," MIS Quarterly, (46: 4) pp.2351-2366. source
  • Luxi Shen, Samuel D. Hirshman (2022) As Wages Increase, Do People Work More or Less? A Wage Frame Effect. Management Science 0(0). source
  • Sudheer Chava, Alexander Oettl, Manpreet Singh (2023) Does a One-Size-Fits-All Minimum Wage Cause Financial Stress for Small Businesses?. Management Science 0(0). source
  • Mitch Downey, Nelson Lind, Jeffrey G. Shrader (2023) Adjusting to Rain Before It Falls. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4697

Work Schedule Improvement⚓︎

  • Saravanan Kesavan, Susan J. Lambert, Joan C. Williams, Pradeep K. Pendem (2022) Doing Well by Doing Good: Improving Retail Store Performance with Responsible Scheduling Practices at the Gap, Inc.. Management Science 0(0). source

Gig Economy⚓︎

  • Yanhui Wu, Feng Zhu (2022) Competition, Contracts, and Creativity: Evidence from Novel Writing in a Platform Market. Management Science 0(0). source

Conflict with Work⚓︎

  • Massimo Magni, Manju K. Ahuja, Chiara Trombini (2022) Excessive Mobile Use and Family-Work Conflict: A Resource Drain Theory Approach to Examine Their Effects on Productivity and Well-Being. Information Systems Research 0(0). source

Cyberloafing⚓︎

  • Qian Chen, et al (2022) How mindfulness decreases cyberloafing at work: a dual-system theory perspective, European Journal of Information Systems. source

Career⚓︎

  • Deng, X., Zaza, S., & Armstrong, D. J. (2023). What Motivates First-generation College Students to Consider an IT Career? An Integrative Perspective. Communications of the Association for Information Systems, 52, pp-pp. source

Wage⚓︎

  • Sumit Agarwal, Meghana Ayyagari, Renáta Kosová (2023) Minimum Wage Increases and Employer Performance: Role of Employer Heterogeneity. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4650

Service Operations⚓︎

  • Andres Musalem, Marcelo Olivares, Daniel Yung (2023) Balancing Agent Retention and Waiting Time in Service Platforms. Operations Research 0(0). https://doi.org/10.1287/opre.2022.2418

Matching Markets⚓︎

  • Lanfei Shi, Siva Viswanathan (2023) Optional Verification and Signaling in Online Matching Markets: Evidence from a Randomized Field Experiment. Information Systems Research 0(0). https://doi.org/10.1287/isre.2022.1194

Addiction⚓︎

  • Isaac Vaghefi, Bogdan Negoita, Liette Lapointe (2022) The Path to Hedonic Information System Use Addiction: A Process Model in the Context of Social Networking Sites. Information Systems Research 0(0). source

Abuse⚓︎

  • Amo, Laura C,; Grijalva, Emily; Herath, Tejaswini; Lemoine, G. James; and Rao, H. Raghav. 2022. "Technological Entitlement: It’s My Technology and I’ll (Ab)Use It How I Want To," MIS Quarterly, (46: 3) pp.1395-1420. source

Bayesian Belief⚓︎

  • Markus M. Möbius, Muriel Niederle, Paul Niehaus, Tanya S. Rosenblat (2022) Managing Self-Confidence: Theory and Experimental Evidence. Management Science 0(0). source
  • Stefanie Brilon, Simona Grassi, Manuel Grieder, Jonathan F. Schulz (2023) Strategic Competition and Self-Confidence. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4688
  • Jeremy Watson, Megan MacGarvie, John McKeon (2022) It Was 50 Years Ago Today: Recording Copyright Term and the Supply of Music. Management Science 0(0). source

Sports⚓︎

  • Bouke Klein Teeselink, Martijn J. van den Assem, Dennie van Dolder (2022) Does Losing Lead to Winning? An Empirical Analysis for Four Sports. Management Science 0(0). source

Financial Technology (Fintech)⚓︎

  • Christoph Herpfer, Aksel Mjøs, Cornelius Schmidt (2022) The Causal Impact of Distance on Bank Lending. Management Science 0(0). source
  • Ng, Evelyn, et al. "The strategic options of fintech platforms: An overview and research agenda." Information Systems Journal (2022). https://doi.org/10.1111/isj.12388
  • Maggie Rong Hu, Xiaoyang Li, Yang Shi, Xiaoquan (Michael) Zhang (2023) Numerological Heuristics and Credit Risk in Peer-to-Peer Lending. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1202

Decision Making⚓︎

  • Joshua Lewis, Daniel Feiler, Ron Adner (2022) The Worst-First Heuristic: How Decision Makers Manage Conjunctive Risk. Management Science 0(0). source
  • Huang, L., & Savary, J. (2022). When Payments Go Social: The Use  of Person-to-Person Payment Methods Attenuates the Endowment Effect. Journal of Marketing Research, 0(0). source
  • Elif Incekara-Hafalir, Grace H. Y. Lee, Audrey K. L. Siah, Erte Xiao (2023) Incentives to Persevere. Management Science 0(0). source
  • Carlos Alós-Ferrer, Michele Garagnani (2023) Part-Time Bayesians: Incentives and Behavioral Heterogeneity in Belief Updating. Management Science 0(0). source
  • Geoffrey Fisher (2023) Measuring the Factors Influencing Purchasing Decisions: Evidence From Cursor Tracking and Cognitive Modeling. Management Science 0(0). source
  • Steffen Künn, Juan Palacios, Nico Pestel (2023) Indoor Air Quality and Strategic Decision Making. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4643
  • Fadong Chen, Zhi Zhu, Qiang Shen, Ian Krajbich, Todd A. Hare (2023) Intrachoice Dynamics Shape Social Decisions. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4732

Team⚓︎

  • Kilcullen, Molly, et al. "Does Team Orientation Matter? A State of the Science Review, Meta‐Analysis and Multilevel Framework." Journal of Organizational Behavior. source
  • Fang, Yulin, Derrick Neufeld, and Xiaojie Zhang. "Knowledge coordination via digital artefacts in highly dispersed teams." Information Systems Journal (2021). source
  • Dennis, Alexander S., Jordan B. Barlow, and Alan R. Dennis. "The Power of Introverts: Personality and Intelligence in Virtual Teams." Journal of Management Information Systems 39.1 (2022): 102-129. source
  • Kearney, E, Razinskas, S, Weiss, M, Hoegl, M. Gender Diversity and Team Performance Under Time Pressure: The Role of Team Withdrawal and Information Elaboration. J Organ Behav. 2022. source
  • Lorens A. Imhof, Matthias Kräkel (2022) Team Diversity and Incentives. Management Science 0(0). source
  • Tat Y. Chan, Yijun Chen, Chunhua Wu (2022) Collaborate to Compete: An Empirical Matching Game Under Incomplete Information in Rank-Order Tournaments. Marketing Science 0(0). source
  • Mullins, Jeffrey K. and Sabherwal, Rajiv. 2022. "Just Enough Information? The Contingent Curvilinear Effect of Information Volume on Decision Performance in IS-Enabled Teams," MIS Quarterly, (46: 4) pp.2197-2228. source

Sharing Economy⚓︎

  • John Tripp, D. Harrison McKnight & Nancy Lankton (2022) What most influences consumers’ intention to use? different motivation and trust stories for uber, airbnb, and taskrabbit, European Journal of Information Systems source
  • Lee, Kyunghee; Jin, Qianran (Jenny); Animesh, Animesh; and Ramaprasad, Jui. 2022. "Impact of Ride-Hailing Services on Transportation Mode Choices: Evidence from Traffic and Transit Ridership," MIS Quarterly, (46: 4) pp.1875-1900. source
  • Hyuck David Chung, Yue Maggie Zhou, Sendil Ethiraj (2023) Platform Governance in the Presence of Within-Complementor Interdependencies: Evidence from the Rideshare Industry. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4706
  • Yingjie Zhang, Beibei Li, Sean Qian (2023) Ridesharing and Digital Resilience for Urban Anomalies: Evidence from the New York City Taxi Market. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1212

IS Use⚓︎

  • Weinert, Christoph, et al. "Repeated IT Interruption: Habituation and Sensitization of User Responses." Journal of Management Information Systems 39.1 (2022): 187-217. source
  • Park, Eun Hee, et al. "Why do Family Members Reject AI in Health Care? Competing Effects of Emotions." Journal of Management Information Systems 39.3 (2022): 765-792. source

Auction⚓︎

  • Yuexin Li, Xiaoyin Ma, Luc Renneboog (2022) In Art We Trust. Management Science 0(0). source

Anti-corruption⚓︎

  • Lily Fang, Josh Lerner, Chaopeng Wu, Qi Zhang (2022) Anticorruption, Government Subsidies, and Innovation: Evidence from China. Management Science 0(0). source

Visual⚓︎

  • Sgourev, S. V., Aadland, E., & Formilan, G. (2022). Relations in Aesthetic Space: How Color Enables Market Positioning. Administrative Science Quarterly, 0(0). source

Versioning⚓︎

  • Yiting Deng, Anja Lambrecht, Yongdong Liu (2022) Spillover Effects and Freemium Strategy in the Mobile App Market. Management Science 0(0). source

Loyalty Program⚓︎

  • Federico Rossi, Pradeep K. Chintagunta (2022) Consumer Loyalty Programs and Retail Prices: Evidence from Gasoline Markets. Marketing Science 0(0). source

Promotion⚓︎

  • Hmurovic, J., Lamberton, C., & Goldsmith, K. (2022). Examining the Efficacy of Time Scarcity Marketing Promotions in Online Retail. Journal of Marketing Research, 0(0). source
  • Øystein Daljord, Carl F. Mela, Jason M. T. Roos, Jim Sprigg, Song Yao (2023) The Design and Targeting of Compliance Promotions. Marketing Science 0(0). https://doi.org/10.1287/mksc.2022.1420

Customization⚓︎

  • Fuchs, M., & Schreier, M. (2023). Paying Twice for Aesthetic Customization? The Negative Effect of Uniqueness on a Product’s Resale Value. Journal of Marketing Research, 0(0). source

Blockchain⚓︎

  • Xia Chen, Qiang Cheng, Ting Luo (2023) The Economic Value of Blockchain Applications: Early Evidence from Asset-Backed Securities. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4671

Donation⚓︎

  • Waites, S. F., Farmer, A., Hasford, J., & Welden, R. (2023). Teach a Man to Fish: The Use of Autonomous Aid in Eliciting Donations. Journal of Marketing Research, 0(0). https://doi.org/10.1177/00222437221140028

Immigration⚓︎

  • Britta Glennon (2023) How Do Restrictions on High-Skilled Immigration Affect Offshoring? Evidence from the H-1B Program. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4715

Production and Collaboration⚓︎

  • Abhishek Deshmane, Victor Martínez-de-Albéniz (2023) Come Together, Right Now? An Empirical Study of Collaborations in the Music Industry. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4743

Education⚓︎

  • Mingyu Chen (2023) The Value of U.S. College Education in Global Labor Markets: Experimental Evidence from China. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4745

IT Systems⚓︎

  • Amrit Tiwana, Hani Safadi (2023) Atrophy in Aging Systems: Evidence, Dynamics, and Antidote. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1218

© License⚓︎

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Last Update On 2023-04-15.

Copyright © 2019 - 2023 Yihong Liu
\ No newline at end of file diff --git a/research/technical/index.html b/research/technical/index.html index d0f4eb6..231d3dc 100644 --- a/research/technical/index.html +++ b/research/technical/index.html @@ -1 +1 @@ - Technical Model / Design Science - MIS Reading List

Technical Model / Design Science⚓︎

On Design Science⚓︎

  • Hevner, Alan R., et al. "Design science in information systems research." MIS quarterly (2004): 75-105. source
  • Peffers, Ken, et al. "A design science research methodology for information systems research." Journal of management information systems 24.3 (2007): 45-77. source
  • Hevner, Alan, et al. "Design science research in information systems." Design research in information systems: theory and practice (2010): 9-22. source
  • Sein, Maung K., et al. "Action design research." MIS quarterly (2011): 37-56. source
  • Gregor, Shirley and Hevner, Alan R.. 2013. "Positioning and Presenting Design Science Research for Maximum Impact," MIS Quarterly, (37: 2) pp.337-355. source
  • Deng, Qi and Ji, Shaobo (2018) "A Review of Design Science Research in Information Systems: Concept, Process, Outcome, and Evaluation," Pacific Asia Journal of the Association for Information Systems: Vol. 10: Iss. 1, Article 2. source
  • Baskerville, Richard, et al. "Design science research contributions: Finding a balance between artifact and theory." Journal of the Association for Information Systems 19.5 (2018): 3. source
  • Maedche, Alexander, et al. "Conceptualization of the problem space in design science research." International conference on design science research in information systems and technology. Springer, Cham, 2019. source
  • Brendel, A. B., & Muntermann, J. (2022). Replication of design theories: Reflections on function, outcome, and impact. Information Systems Journal, 1– 19. source
  • Nagle, T., Doyle, C., Alhassan, I. M., & Sammon, D. (2022). The Research Method we Need or Deserve? A Literature Review of the Design Science Research Landscape. Communications of the Association for Information Systems, 50, pp-pp. source

Artificial Intelligence⚓︎

  • Nguyen, Q. N., Sidorova, A., & Torres, R. (2022). Artificial Intelligence in Business: A Literature Review and Research Agenda. Communications of the Association for Information Systems, 50, pp-pp. source

Deep Learning⚓︎

  • Luyang Chen, Markus Pelger, Jason Zhu (2023) Deep Learning in Asset Pricing. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4695
  • Samtani, S., Zhu, H., Padmanabhan, B., Chai, Y., & Chen, H. (2023). Deep learning for information systems research. Journal of Management Information Systems. https://doi.org/10.1080/07421222.2023.2172772

Reinforcement Learning⚓︎

  • Kaelbling, Leslie Pack, Michael L. Littman, and Andrew W. Moore. "Reinforcement learning: A survey." Journal of artificial intelligence research 4 (1996): 237-285. source
  • Arulkumaran, Kai, et al. "Deep reinforcement learning: A brief survey." IEEE Signal Processing Magazine 34.6 (2017): 26-38. source
  • Li, Yuxi. "Deep reinforcement learning." arXiv preprint arXiv:1810.06339 (2018). source
  • Li, Yuxi. "Reinforcement learning applications." arXiv preprint arXiv:1908.06973 (2019). source
  • Liebman, Elad, Maytal Saar-Tsechansky, and Peter Stone. "The Right Music at the Right Time: Adaptive Personalized Playlists Based on Sequence Modeling." MIS Quarterly 43.3 (2019). source
  • Wang, Hao-nan, et al. "Deep reinforcement learning: a survey." Frontiers of Information Technology & Electronic Engineering (2020): 1-19. source
  • Parker-Holder, Jack, et al. "Automated Reinforcement Learning (AutoRL): A Survey and Open Problems." arXiv preprint arXiv:2201.03916 (2022). source
  • Mark Sellke, Aleksandrs Slikvins (2022) The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity. Operations Research 0(0). source
  • Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu (2023) Nonstationary Reinforcement Learning: The Blessing of (More) Optimism. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4704
  • Yi Zhu, Jing Dong, Henry Lam (2023) Uncertainty Quantification and Exploration for Reinforcement Learning. Operations Research 0(0). https://doi.org/10.1287/opre.2023.2436

Self-Supervised Learning⚓︎

  • Jing, Longlong, and Yingli Tian. "Self-supervised visual feature learning with deep neural networks: A survey." IEEE transactions on pattern analysis and machine intelligence 43.11 (2020): 4037-4058. source
  • Xie, Yaochen, et al. "Self-supervised learning of graph neural networks: A unified review." arXiv preprint arXiv:2102.10757 (2021). source
  • Liu, Yixin, et al. "Graph self-supervised learning: A survey." arXiv preprint arXiv:2103.00111 (2021). source
  • Jaiswal, Ashish, et al. "A survey on contrastive self-supervised learning." Technologies 9.1 (2021): 2. source
  • Liu, Xiao, et al. "Self-supervised learning: Generative or contrastive." IEEE Transactions on Knowledge and Data Engineering (2021). source

Transfer Learning⚓︎

  • Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359. source
  • Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. "A survey of transfer learning." Journal of Big data 3.1 (2016): 1-40. source
  • Tan, Chuanqi, et al. "A survey on deep transfer learning." International conference on artificial neural networks. Springer, Cham, 2018. source
  • Zhuang, Fuzhen, et al. "A comprehensive survey on transfer learning." Proceedings of the IEEE 109.1 (2020): 43-76. source

Differential Privacy⚓︎

  • Dwork, Cynthia, et al. "Calibrating noise to sensitivity in private data analysis." Theory of cryptography conference. Springer, Berlin, Heidelberg, 2006. source
  • Zheng, Qinqing, et al. "Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion." arXiv preprint arXiv:2003.04493 (2020). source
  • Goodfellow, Ian. "Efficient per-example gradient computations." arXiv preprint arXiv:1510.01799 (2015). source
  • Abadi, Martin, et al. "Deep learning with differential privacy." Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. 2016. source
  • Mironov, Ilya. "Rényi differential privacy." 2017 IEEE 30th Computer Security Foundations Symposium (CSF). IEEE, 2017. source
  • McMahan, H. Brendan, et al. "A general approach to adding differential privacy to iterative training procedures." arXiv preprint arXiv:1812.06210 (2018). source
  • Mironov, Ilya, Kunal Talwar, and Li Zhang. "Rényi Differential Privacy of the Sampled Gaussian Mechanism." arXiv preprint arXiv:1908.10530 (2019). source
  • Dwork, Cynthia, and Aaron Roth. "The algorithmic foundations of differential privacy." Foundations and Trends in Theoretical Computer Science 9.3-4 (2014): 211-407. source
  • Dwork, Cynthia, and Adam Smith. "Differential privacy for statistics: What we know and what we want to learn." Journal of Privacy and Confidentiality 1.2 (2010). source
  • Ji, Zhanglong, Zachary C. Lipton, and Charles Elkan. "Differential privacy and machine learning: a survey and review." arXiv preprint arXiv:1412.7584 (2014). source
  • Jiang, Honglu, et al. "Differential Privacy and Its Applications in Social Network Analysis: A Survey." arXiv preprint arXiv:2010.02973 (2020). source
  • Yang, Mengmeng, et al. "Local differential privacy and its applications: A comprehensive survey." arXiv preprint arXiv:2008.03686 (2020). source

Explainable ML / DL / AI⚓︎

  • Angelino, Elaine, et al. "Learning certifiably optimal rule lists for categorical data." arXiv preprint arXiv:1704.01701 (2017). source
  • Lundberg, Scott M., and Su-In Lee. "A unified approach to interpreting model predictions." Advances in neural information processing systems 30 (2017). source
  • Lipton, Zachary C. "The mythos of model interpretability." Queue 16.3 (2018): 31-57. source
  • Lundberg, Scott M., et al. "From local explanations to global understanding with explainable AI for trees." Nature machine intelligence 2.1 (2020): 56-67. source
  • Molnar, Christoph. Interpretable machine learning. 2020. source
  • Arrieta, Alejandro Barredo, et al. "Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI." Information Fusion 58 (2020): 82-115. source
  • Wang, Zhuo, et al. "Scalable Rule-Based Representation Learning for Interpretable Classification." arXiv preprint arXiv:2109.15103 (2021). source
  • Chen, Valerie, et al. "Interpretable machine learning: Moving from mythos to diagnostics." Communications of the ACM, August 2022, Vol. 65 No. 8, Pages 43-50. source

Fairness⚓︎

  • Aumüller, Martin, Rasmus Pagh, and Francesco Silvestri. "Fair near neighbor search: Independent range sampling in high dimensions." Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems. 2020. source
  • Krakovsky, Marina. "## Formalizing Fairness." Communications of the ACM, August 2022, Vol. 65 No. 8, Pages 11-13. source
  • Dong, Yushun, et al. "Fairness in Graph Mining: A Survey." arXiv preprint arXiv:2204.09888 (2022). source

Active Learning⚓︎

  • Aggarwal, C. C., Kong, X., Gu, Q., Han, J., & Yu, P. S. (2014). "Active learning: A survey". In Data Classification: Algorithms and Applications (pp. 571-605). CRC Press. source
  • Ren, Pengzhen, et al. "A Survey of Deep Active Learning." ArXiv:2009.00236 [Cs, Stat], Aug. 2020. arXiv.org. source
  • Atahan, Pelin, and Sumit Sarkar. "Accelerated learning of user profiles." Management Science 57.2 (2011): 215-239. source

Label Imbalance⚓︎

  • Nasir, Murtaza, et al. "Improving Imbalanced Machine Learning with Neighborhood-Informed Synthetic Sample Placement." Journal of Management Information Systems 39.4 (2022): 1116-1145. https://doi.org/10.1080/07421222.2022.2127453

Label Noise⚓︎

  • Han, Bo, et al. "A survey of label-noise representation learning: Past, present and future." arXiv preprint arXiv:2011.04406 (2020). source

Natural Language Processing⚓︎

Text Summarization⚓︎

  • Rush, Alexander M., Sumit Chopra, and Jason Weston. "A neural attention model for abstractive sentence summarization." arXiv preprint arXiv:1509.00685 (2015). source
  • Chen, Yen-Chun, and Mohit Bansal. "Fast abstractive summarization with reinforce-selected sentence rewriting." arXiv preprint arXiv:1805.11080 (2018). source
  • Gehrmann, Sebastian, Yuntian Deng, and Alexander M. Rush. "Bottom-up abstractive summarization." arXiv preprint arXiv:1808.10792 (2018). source

Topic Modeling⚓︎

  • Jelodar, Hamed, et al. "Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey." Multimedia Tools and Applications 78.11 (2019): 15169-15211. source
  • Qiang, Jipeng, et al. "Short text topic modeling techniques, applications, and performance: a survey." IEEE Transactions on Knowledge and Data Engineering (2020). source
  • Vayansky, Ike, and Sathish AP Kumar. "A review of topic modeling methods." Information Systems 94 (2020): 101582. source
  • Kherwa, Pooja, and Poonam Bansal. "Topic modeling: a comprehensive review." EAI Endorsed transactions on scalable information systems 7.24 (2020). source
  • Chauhan, Uttam, and Apurva Shah. "Topic Modeling Using Latent Dirichlet allocation: A Survey." ACM Computing Surveys (CSUR) 54.7 (2021): 1-35. source
  • Yi Yang, Kunpeng Zhang, Yangyang Fan (2022) sDTM: A Supervised Bayesian Deep Topic Model for Text Analytics. Information Systems Research 0(0). source
  • Li, Weifeng and Chen, Hsinchun. 2022. "Discovering Emerging Threats in the Hacker Community: A Nonparametric Emerging Topic Detection Framework," MIS Quarterly, (46: 4) pp.2337-2350. source

Personalized Feedback⚓︎

  • Jiyeon Hong, Paul R. Hoban (2022) Writing More Compelling Creative Appeals: A Deep Learning-Based Approach. Marketing Science 0(0). source

Sentiment Analysis⚓︎

  • Rocklage, M. D., He, S., Rucker, D. D., & Nordgren, L. F. (2023). Beyond Sentiment: The Value and Measurement of Consumer Certainty in Language. Journal of Marketing Research, 0(0). https://doi.org/10.1177/00222437221134802

Decentralized Learning⚓︎

  • Li, Tian, et al. "Federated learning: Challenges, methods, and future directions." IEEE Signal Processing Magazine 37.3 (2020): 50-60. source
  • Lim, Wei Yang Bryan, et al. "Federated learning in mobile edge networks: A comprehensive survey." IEEE Communications Surveys & Tutorials 22.3 (2020): 2031-2063. source
  • Mothukuri, Viraaji, et al. "A survey on security and privacy of federated learning." Future Generation Computer Systems 115 (2021): 619-640. source
  • Kairouz, Peter, et al. "Advances and open problems in federated learning." Foundations and Trends® in Machine Learning 14.1–2 (2021): 1-210. source
  • Warnat-Herresthal, Stefanie, et al. "Swarm learning for decentralized and confidential clinical machine learning." Nature 594.7862 (2021): 265-270. source code
  • Kallista Bonawitz, et al. 2022. Federated learning and privacy. Commun. ACM 65, 4 (April 2022), 90–97. source

Personality Measurement⚓︎

  • Kai Yang, Raymond Y. K. Lau, Ahmed Abbasi (2022) Getting Personal: A Deep Learning Artifact for Text-Based Measurement of Personality. Information Systems Research 0(0). source

Adversaries⚓︎

  • Li, Weifeng, and Yidong Chai. "Assessing and Enhancing Adversarial Robustness of Predictive Analytics: An Empirically Tested Design Framework." Journal of Management Information Systems 39.2 (2022): 542-572. source

Data Imputation⚓︎

  • Lin, Wei-Chao, and Chih-Fong Tsai. "Missing value imputation: a review and analysis of the literature (2006–2017)." Artificial Intelligence Review 53.2 (2020): 1487-1509. source
  • Hasan, Md Kamrul, et al. "Missing value imputation affects the performance of machine learning: A review and analysis of the literature (2010–2021)." Informatics in Medicine Unlocked 27 (2021): 100799. source

Application⚓︎

  • Aiken, Emily, et al. "Machine learning and phone data can improve targeting of humanitarian aid." Nature (2022): 1-7. source
  • Nan Zhang, Heng Xu (2023) Fairness of Ratemaking for Catastrophe Insurance: Lessons from Machine Learning. Information Systems Research 0(0). https://doi.org/10.1287/isre.2022.1195
  • Arindam Ray, Wolfgang Jank, Kaushik Dutta, Matthew Mullarkey (2023) An LSTM+ Model for Managing Epidemics: Using Population Mobility and Vulnerability for Forecasting COVID-19 Hospital Admissions. INFORMS Journal on Computing 0(0). https://doi.org/10.1287/ijoc.2023.1269

Conversational Agents⚓︎

  • Elshan, E., Ebel, P., Söllner, M., & Leimeister, J. M. (2023). Leveraging Low Code Development of Smart Personal Assistants: An Integrated Design Approach with the SPADE Method. Journal of Management Information Systems, 40(1), 96-129. https://doi.org/10.1080/07421222.2023.2172776

Transparency⚓︎

  • Bitzer, T., Wiener, M., & Cram, W. (2023). Algorithmic Transparency: Concepts, Antecedents, and Consequences – A Review and Research Framework. Communications of the Association for Information Systems, 52, pp-pp. https://aisel.aisnet.org/cais/vol52/iss1/16

Graph And Network⚓︎

Graph Neural Network⚓︎

  • Kipf, T. N. "Deep learning with graph-structured representations." (2020). pdf
  • Wu, Zonghan, et al. "A comprehensive survey on graph neural networks." IEEE Transactions on Neural Networks and Learning Systems (2020). source
  • Zhou, Jie, et al. "Graph neural networks: A review of methods and applications." arXiv preprint arXiv:1812.08434 (2018). source
  • Zhang, Chuxu, et al. "Heterogeneous graph neural network." Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019. source
  • Wang, Xiao, et al. "Heterogeneous graph attention network." The World Wide Web Conference. 2019. source
  • Hu, Ziniu, et al. "Heterogeneous graph transformer." Proceedings of The Web Conference 2020. 2020. source

Graph Embedding⚓︎

  • Goyal, Palash, and Emilio Ferrara. "Graph embedding techniques, applications, and performance: A survey." Knowledge-Based Systems 151 (2018): 78-94. source
  • Xi Chen, Yan Liu, Cheng Zhang (2022) Distinguishing Homophily from Peer Influence Through Network Representation Learning. INFORMS Journal on Computing 0(0). source

Graphical Causality⚓︎

  • Bernhard Schölkopf, et al. "Towards Causal Representation Learning." (2021). source

Influence Maximization⚓︎

  • Li, Yuchen, et al. "Influence maximization on social graphs: A survey." IEEE Transactions on Knowledge and Data Engineering 30.10 (2018): 1852-1872. source
  • Banerjee, Suman, Mamata Jenamani, and Dilip Kumar Pratihar. "A survey on influence maximization in a social network." Knowledge and Information Systems 62.9 (2020): 3417-3455. source
  • De Nittis, Giuseppe, and Nicola Gatti. "How to maximize the spread of social influence: A survey." arXiv preprint arXiv:1806.07757 (2018). source
  • Ozan Candogan (2022) Persuasion in Networks: Public Signals and Cores. Operations Research 0(0). source

Vertical Markets⚓︎

  • Soheil Ghili (2022) Network Formation and Bargaining in Vertical Markets: The Case of Narrow Networks in Health Insurance. Marketing Science 0(0). source

Network Structures⚓︎

  • Sinan Aral, Paramveer S. Dhillon (2022) What (Exactly) Is Novelty in Networks? Unpacking the Vision Advantages of Brokers, Bridges, and Weak Ties. Management Science 0(0). source
  • Schecter, Aaron, Omid Nohadani, and Noshir Contractor. "A Robust Inference Method for Decision Making in Networks." Management Information Systems Quarterly 46.2 (2022): 713-738. source
  • Syngjoo Choi, Sanjeev Goyal, Frederic Moisan, Yu Yang Tony To (2023) Learning in Networks: An Experiment on Large Networks with Real-World Features. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4680

Network Privacy⚓︎

  • Marcella Hastings, Brett Hemenway Falk, Gerry Tsoukalas (2022) Privacy-Preserving Network Analytics. Management Science 0(0). source

Recommendation Systems⚓︎

Recommendation Objectives⚓︎

  • Gunawardana, Asela, and Guy Shani. "A survey of accuracy evaluation metrics of recommendation tasks." Journal of Machine Learning Research 10.12 (2009). source
  • Kunaver, Matevž, and Tomaž Požrl. "Diversity in recommender systems–A survey." Knowledge-based systems 123 (2017): 154-162. source
  • Kaminskas, Marius, and Derek Bridge. "Diversity, serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems." ACM Transactions on Interactive Intelligent Systems (TiiS) 7.1 (2016): 1-42. source
  • Wu, Qiong, et al. "Recent advances in diversified recommendation." arXiv preprint arXiv:1905.06589 (2019). source
  • Wu, Le, et al. "A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation." IEEE Transactions on Knowledge and Data Engineering (2022). source
  • Alhijawi, Bushra, Arafat Awajan, and Salam Fraihat. "Survey on the Objectives of Recommender System: Measures, Solutions, Evaluation Methodology, and New Perspectives." ACM Computing Surveys (CSUR) (2022). source

Dataset⚓︎

  • Gao, Chongming, et al. "KuaiRec: A Fully-observed Dataset for Recommender Systems." arXiv preprint arXiv:2202.10842 (2022). source web
  • Chin, Jin Yao, Yile Chen, and Gao Cong. "The Datasets Dilemma: How Much Do We Really Know About Recommendation Datasets?." Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. 2022. source
  • Lü, Linyuan, and Tao Zhou. "Link prediction in complex networks: A survey." Physica A: statistical mechanics and its applications 390.6 (2011): 1150-1170. source
  • Lü, Linyuan, and Tao Zhou. "Link prediction in complex networks: A survey." Physica A: statistical mechanics and its applications 390.6 (2011): 1150-1170. https://doi.org/10.1145/3012704
  • Wang, P., Xu, B., Wu, Y. et al. Link prediction in social networks: the state-of-the-art. Sci. China Inf. Sci. 58, 1–38 (2015). https://doi.org/10.1007/s11432-014-5237-y
  • Kim, J., Diesner, J. Formational bounds of link prediction in collaboration networks. Scientometrics 119, 687–706 (2019). https://doi.org/10.1007/s11192-019-03055-6
  • Kumar, Ajay, et al. "Link prediction techniques, applications, and performance: A survey." Physica A: Statistical Mechanics and its Applications 553 (2020): 124289. source
  • Qin, Meng, and Dit-Yan Yeung. "Temporal Link Prediction: A Unified Framework, Taxonomy, and Review." arXiv preprint arXiv:2210.08765 (2022). https://doi.org/10.48550/arXiv.2210.08765
  • Wu, H., Song, C., Ge, Y. et al. Link Prediction on Complex Networks: An Experimental Survey. Data Sci. Eng. 7, 253–278 (2022). https://doi.org/10.1007/s41019-022-00188-2

Recommendation Framework⚓︎

  • Anelli, Vito Walter, et al. "Elliot: a comprehensive and rigorous framework for reproducible recommender systems evaluation." Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2021. source code
  • TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale recommender systems (RecSys). It allows authors to train models with large embedding tables sharded across many GPUs.
  • Spotlight uses PyTorch to build both deep and shallow recommender models. By providing both a slew of building blocks for loss functions (various pointwise and pairwise ranking losses), representations (shallow factorization representations, deep sequence models), and utilities for fetching (or generating) recommendation datasets, it aims to be a tool for rapid exploration and prototyping of new recommender models.
  • MTReclib provides a PyTorch implementation of multi-task recommendation models and common datasets.
  • The repository microsoft/recommenders contains examples and best practices for building recommendation systems, provided as Jupyter notebooks.
  • Surprise is a Python scikit for building and analyzing recommender systems that deal with explicit rating data.
  • The repository hiroyuki-kasai/NMFLibrary is a pure-Matlab library of a collection of algorithms of non-negative matrix factorization (NMF).
  • QMF is a fast and scalable C++ library for implicit-feedback matrix factorization models (WALS and BPR).
  • The repository benfred/implicit provides fast Python implementations of several different popular recommendation algorithms for implicit feedback datasets.
  • This repository liu-yihong/BPRH implements the Bayesian personalized ranking method for heterogeneous implicit feedback.
  • reXmeX is recommender system evaluation metric library. It consists of utilities for recommender system evaluation. First, it provides a comprehensive collection of metrics for the evaluation of recommender systems. Second, it includes a variety of methods for reporting and plotting the performance results. Implemented metrics cover a range of well-known metrics and newly proposed metrics from data mining conferences and prominent journals.

Sequential Recommendation Systems⚓︎

  • Quadrana, Massimo, Paolo Cremonesi, and Dietmar Jannach. "Sequence-aware recommender systems." ACM Computing Surveys (CSUR) 51.4 (2018): 1-36. source
  • Maher, Mohamed, et al. "Comprehensive Empirical Evaluation of Deep Learning Approaches for Session-based Recommendation in E-Commerce." arXiv preprint arXiv:2010.12540 (2020). source
  • Fang, Hui, et al. "Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations." ACM Transactions on Information Systems (TOIS) 39.1 (2020): 1-42. source
  • Latifi, Sara, Noemi Mauro, and Dietmar Jannach. "Session-aware recommendation: A surprising quest for the state-of-the-art." Information Sciences 573 (2021): 291-315. source
  • Wang, Shoujin, et al. "A survey on session-based recommender systems." ACM Computing Surveys (CSUR) 54.7 (2021): 1-38. source
  • Wen Wang, Beibei Li, Xueming Luo, Xiaoyi Wang (2022) Deep Reinforcement Learning for Sequential Targeting. Management Science 0(0). source
  • Omid Rafieian (2022) Optimizing User Engagement Through Adaptive Ad Sequencing. Marketing Science 0(0). source
  • Yifu Li, Christopher Thomas Ryan, Lifei Sheng (2023) Optimal Sequencing in Single-Player Games. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4665
  • Marios Kokkodis, Panagiotis G. Ipeirotis (2023) The Good, the Bad, and the Unhirable: Recommending Job Applicants in Online Labor Markets. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4690

User-Item Matrix Factorization⚓︎

  • Su, Xiaoyuan, and Taghi M. Khoshgoftaar. "A survey of collaborative filtering techniques." Advances in artificial intelligence 2009 (2009). source
  • Cacheda, Fidel, et al. "Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems." ACM Transactions on the Web (TWEB) 5.1 (2011): 1-33. source
  • Shi, Yue, Martha Larson, and Alan Hanjalic. "Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges." ACM Computing Surveys (CSUR) 47.1 (2014): 1-45. source
  • Han, Soyeon Caren, et al. "GLocal-K: Global and Local Kernels for Recommender Systems." Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2021. source
  • Rendle, Steffen, et al. "Neural collaborative filtering vs. matrix factorization revisited." Fourteenth ACM conference on recommender systems. 2020. source

Graph Neural Network Based Recommendation⚓︎

  • Wu, Shiwen, et al. "Graph neural networks in recommender systems: a survey." arXiv preprint arXiv:2011.02260 (2020). source

Reinforcement Learning Based Recommendation⚓︎

  • Lin, Yuanguo, et al. "A Survey on Reinforcement Learning for Recommender Systems." arXiv preprint arXiv:2109.10665 (2021). source

Causal Learning⚓︎

  • Si, Zihua et al. “A Model-Agnostic Causal Learning Framework for Recommendation using Search Data.” (2022). source code

Self-Supervised Learning⚓︎

  • Yu, Junliang, et al. "Self-Supervised Learning for Recommender Systems: A Survey." arXiv preprint arXiv:2203.15876 (2022). source

Debias⚓︎

  • Schnabel, Tobias, et al. "Recommendations as treatments: Debiasing learning and evaluation." international conference on machine learning. PMLR, 2016. source
  • Chen, Jiawei, et al. "AutoDebias: Learning to Debias for Recommendation." arXiv preprint arXiv:2105.04170 (2021). source
  • Jiawei Chen on github.com provides a repository at jiawei-chen/RecDebiasing

User Reviews for Recommendation⚓︎

  • Sachdeva, Noveen, and Julian McAuley. "How useful are reviews for recommendation? a critical review and potential improvements." Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020. source

Regulations⚓︎

  • Tommaso Di Noia, et al. 2022. Recommender systems under European AI regulations. Commun. ACM 65, 4 (April 2022), 69–73. source

Healthcare⚓︎

  • Ali Hajjar, Oguzhan Alagoz (2022) Personalized Disease Screening Decisions Considering a Chronic Condition. Management Science 0(0). source
  • Xiang Hui, Zekun Liu, Weiqing Zhang (2023) From High Bar to Uneven Bars: The Impact of Information Granularity in Quality Certification. Management Science 0(0). https://pubsonline.informs.org/doi/abs/10.1287/isre.2022.1191
  • Josh C. D’Aeth, Shubhechyya Ghosal, Fiona Grimm, David Haw, Esma Koca, Krystal Lau, Huikang Liu, Stefano Moret, Dheeya Rizmie, Peter C. Smith, Giovanni Forchini, Marisa Miraldo, Wolfram Wiesemann (2023) Optimal Hospital Care Scheduling During the SARS-CoV-2 Pandemic. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4679
  • Johnson, M., Murthy, D., Robertson, B. W., Smith, W. R., & Stephens, K. K. (2023). Moving Emergency Response Forward: Leveraging Machine-Learning Classification of Disaster-Related Images Posted on Social Media. Journal of Management Information Systems, 40(1), 163-182. https://doi.org/10.1080/07421222.2023.2172778

Point-of-Interest⚓︎

  • Xiao-Jun Wang, Tao Liu, Weiguo Fan (2023) TGVx: Dynamic Personalized POI Deep Recommendation Model. INFORMS Journal on Computing 0(0). https://doi.org/10.1287/ijoc.2023.1286

Explainable Recommendation⚓︎

  • Zhang, Yongfeng, and Xu Chen. "Explainable recommendation: A survey and new perspectives." Foundations and Trends in Information Retrieval 14.1 (2020): 1-101. source
  • Chen, Xu, Yongfeng Zhang, and Ji-Rong Wen. "Measuring" Why" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendation." arXiv preprint arXiv:2202.06466 (2022). source

Attacking Recommendation Systems⚓︎

  • Su, Xue-Feng, Hua-Jun Zeng, and Zheng Chen. "Finding group shilling in recommendation system." Special interest tracks and posters of the 14th international conference on World Wide Web. 2005. source
  • O'Donovan, John, and Barry Smyth. "Is trust robust? An analysis of trust-based recommendation." Proceedings of the 11th international conference on Intelligent user interfaces. 2006. source
  • Hurley, Neil J., Michael P. O'Mahony, and Guenole CM Silvestre. "Attacking recommender systems: A cost-benefit analysis." IEEE Intelligent Systems 22.3 (2007): 64-68. source
  • Patel, Krupa, et al. "A state of art survey on shilling attack in collaborative filtering based recommendation system." Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 1. Springer, Cham, 2016. source
  • Fang, Minghong, et al. "Poisoning attacks to graph-based recommender systems." Proceedings of the 34th Annual Computer Security Applications Conference. 2018. source
  • Hu, Rui, et al. "Targeted poisoning attacks on social recommender systems." 2019 IEEE Global Communications Conference (GLOBECOM). IEEE, 2019. source
  • Zhang, Hengtong, et al. "Practical data poisoning attack against next-item recommendation." Proceedings of The Web Conference 2020. 2020. source
  • Song, Junshuai, et al. "Poisonrec: an adaptive data poisoning framework for attacking black-box recommender systems." 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 2020. source
  • Wu, Zih-Wun, Chiao-Ting Chen, and Szu-Hao Huang. "Poisoning attacks against knowledge graph-based recommendation systems using deep reinforcement learning." Neural Computing and Applications (2021): 1-19. source
  • Chen, Liang, et al. "Data poisoning attacks on neighborhood‐based recommender systems." Transactions on Emerging Telecommunications Technologies 32.6 (2021): e3872. source
  • Zhang, Hengtong, et al. "Data Poisoning Attack against Recommender System Using Incomplete and Perturbed Data." Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2021. source
  • Fan, Wenqi, et al. "Attacking Black-box Recommendations via Copying Cross-domain User Profiles." 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021. source

Diversity⚓︎

  • Kexin Yin, Xiao Fang, Bintong Chen, Olivia R. Liu Sheng (2022) Diversity Preference-Aware Link Recommendation for Online Social Networks. Information Systems Research 0(0). source

Multi-Sided⚓︎

  • Rastegari, Baharak, et al. "Two-sided matching with partial information." Proceedings of the fourteenth ACM conference on Electronic Commerce. 2013. https://doi.org/10.1145/2482540.2482607
  • Malgonde, Onkar, et al. "TAMING COMPLEXITY IN SEARCH MATCHING: TWO-SIDED RECOMMENDER SYSTEMS ON DIGITAL PLATFORMS." Mis Quarterly 44.1 (2020). https://aisel.aisnet.org/misq/vol44/iss1/5/
  • Malgonde, Onkar S., et al. "Managing Digital Platforms with Robust Multi-Sided Recommender Systems." Journal of Management Information Systems 39.4 (2022): 938-968. https://doi.org/10.1080/07421222.2022.2127440

Followee Recommendation⚓︎

  • Yaxuan Ran, Jiani Liu, Yishi Zhang (2023) Integrating Users’ Contextual Engagements with Their General Preferences: An Interpretable Followee Recommendation Method. INFORMS Journal on Computing 0(0). https://doi.org/10.1287/ijoc.2023.1284

Reference Learning⚓︎

  • Jiapeng Liu, Miłosz Kadziński, Xiuwu Liao (2023) Modeling Contingent Decision Behavior: A Bayesian Nonparametric Preference-Learning Approach. INFORMS Journal on Computing 0(0). https://doi.org/10.1287/ijoc.2023.1292

Operations Research⚓︎

Lagrangian Relaxation⚓︎

  • Fisher, Marshall L. "The Lagrangian relaxation method for solving integer programming problems." Management science 27.1 (1981): 1-18. source
  • Ghoshal, Abhijeet, et al. "Hiding Sensitive Information when Sharing Distributed Transactional Data." Information systems research 31.2 (2020): 473-490. source

Column Generation⚓︎

  • Menon, Syam, and Sumit Sarkar. "Privacy and Big Data: Scalable Approaches to Sanitize Large Transactional Databases for Sharing." MIS Quarterly 40.4 (2016). source
  • Dash, Sanjeeb, Oktay Günlük, and Dennis Wei. "Boolean decision rules via column generation." arXiv preprint arXiv:1805.09901 (2018). source

Decision Under Uncertainty⚓︎

  • Sen, Suvrajeet, and Julia L. Higle. "An introductory tutorial on stochastic linear programming models." Interfaces 29.2 (1999): 33-61. source
  • Alessio Trivella, Danial Mohseni-Taheri, Selvaprabu Nadarajah (2022) Meeting Corporate Renewable Power Targets. Management Science 0(0). source

Data-Driven Optimization⚓︎

  • Gah-Yi Ban, Cynthia Rudin (2018) The Big Data Newsvendor: Practical Insights from Machine Learning. Operations Research 67(1):90-108. source
  • L. Jeff Hong, Zhiyuan Huang, Henry Lam (2020) Learning-Based Robust Optimization: Procedures and Statistical Guarantees. Management Science 67(6):3447-3467. source
  • Dimitris Bertsimas, Nihal Koduri (2021) Data-Driven Optimization: A Reproducing Kernel Hilbert Space Approach. Operations Research 70(1):454-471. source
  • Keliang Wang, Leonardo Lozano, Carlos Cardonha, David Bergman (2023) Optimizing over an Ensemble of Trained Neural Networks. INFORMS Journal on Computing 0(0). https://doi.org/10.1287/ijoc.2023.1285
  • Omar Besbes, Omar Mouchtaki (2023) How Big Should Your Data Really Be? Data-Driven Newsvendor: Learning One Sample at a Time. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4725

Predict-then-Optimize Paradigm⚓︎

  • Bertsimas, D., & Kallus, N. (2018). From Predictive to Prescriptive Analytics. ArXiv:1402.5481. source
  • Demirovic, E., Stuckey, P. J., Bailey, J., Chan, J., Leckie, C., Ramamohanarao, K., & Guns, T. (2019). Predict+Optimise with Ranking Objectives: Exhaustively Learning Linear Functions. 1078–1085.
  • Elmachtoub, A. N., & Grigas, P. (2020). Smart “Predict, then Optimize.” ArXiv:1710.08005. source
  • Mandi, J., Bucarey, V., Mulamba, M., & Guns, T. (2022). Predict and Optimize: Through the Lens of Learning to Rank. ArXiv:2112.03609. source

Multi-Objective Optimization⚓︎

  • Arne Herzel, Stefan Ruzika, Clemens Thielen (2021) Approximation Methods for Multiobjective Optimization Problems: A Survey. INFORMS Journal on Computing 33(4):1284-1299. source

E-Commerce⚓︎

  • Maximilian Schiffer, Nils Boysen, Patrick S. Klein, Gilbert Laporte, Marco Pavone (2022) Optimal Picking Policies in E-Commerce Warehouses. Management Science 0(0). source
  • Hanwei Li, David Simchi-Levi, Michelle Xiao Wu, Weiming Zhu (2022) Estimating and Exploiting the Impact of Photo Layout: A Structural Approach. Management Science 0(0). source
  • Goldstein, Anat; Oestreicher-Singer, Gal; Barzilay, Ohad; and Yahav, Inbal. 2022. "Are We There Yet? Analyzing Progress in the Conversion Funnel Using the Diversity of Searched Products," MIS Quarterly, (46: 4) pp.2015-2054. source

Assortment Optimization⚓︎

  • Zhen-Yu Chen, Zhi-Ping Fan, Minghe Sun (2022) Machine Learning Methods for Data-Driven Demand Estimation and Assortment Planning Considering Cross-Selling and Substitutions. INFORMS Journal on Computing 0(0). source
  • Santiago R. Balseiro, Antoine Désir (2022) Incentive-Compatible Assortment Optimization for Sponsored Products. Management Science 0(0). source
  • Antoine Désir, Vineet Goyal, Bo Jiang, Tian Xie, Jiawei Zhang (2023) Robust Assortment Optimization Under the Markov Chain Choice Model. Operations Research 0(0). https://doi.org/10.1287/opre.2022.2420
  • Ningyuan Chen, Andre A. Cire, Ming Hu, Saman Lagzi (2023) Model-Free Assortment Pricing with Transaction Data. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4651

Decision Analysis⚓︎

  • Eric Neyman, Tim Roughgarden (2023) From Proper Scoring Rules to Max-Min Optimal Forecast Aggregation. Operations Research 0(0). source
  • Ibrahim Abada, Xavier Lambin (2023) Artificial Intelligence: Can Seemingly Collusive Outcomes Be Avoided?. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4623
  • Asa B. Palley, Ville A. Satopää (2023) Boosting the Wisdom of Crowds Within a Single Judgment Problem: Weighted Averaging Based on Peer Predictions. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4648

Electric Vehicle⚓︎

  • Wei Qi, Yuli Zhang, Ningwei Zhang (2023) Scaling Up Electric-Vehicle Battery Swapping Services in Cities: A Joint Location and Repairable-Inventory Model. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4731

Probabilistic Reasoning⚓︎

  • Li, Zhepeng, et al. "Utility-based link recommendation for online social networks." Management Science 63.6 (2017): 1938-1952. source
  • Ghoshal, Abhijeet, Syam Menon, and Sumit Sarkar. "Recommendations using information from multiple association rules: A probabilistic approach." Information Systems Research 26.3 (2015): 532-551. source

Agent-based Modeling & Simulation⚓︎

  • Bonabeau, Eric. "Agent-based modeling: Methods and techniques for simulating human systems." Proceedings of the national academy of sciences 99.suppl 3 (2002): 7280-7287. source
  • Macal, Charles M., and Michael J. North. "Tutorial on agent-based modeling and simulation." Proceedings of the Winter Simulation Conference, 2005.. IEEE, 2005. source
  • Railsback, Steven F., Steven L. Lytinen, and Stephen K. Jackson. "Agent-based simulation platforms: Review and development recommendations." Simulation 82.9 (2006): 609-623. source
  • An, Li. "Modeling human decisions in coupled human and natural systems: Review of agent-based models." Ecological Modelling 229 (2012): 25-36. source
  • Abar, Sameera, et al. "Agent Based Modelling and Simulation tools: A review of the state-of-art software." Computer Science Review 24 (2017): 13-33. source
  • Mladenov, Martin, et al. "RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems." arXiv preprint arXiv:2103.08057 (2021). source official webiste github
  • Dong, John Qi (2022) "Using Simulation in Information Systems Research," Journal of the Association for Information Systems, 23(2), 408-417. source
  • Zhaolin Hu, L. Jeff Hong (2022) Robust Simulation with Likelihood-Ratio Constrained Input Uncertainty. INFORMS Journal on Computing 0(0). source
  • Lucy E. Morgan, Luke Rhodes-Leader, Russell R. Barton (2022) Reducing and Calibrating for Input Model Bias in Computer Simulation. INFORMS Journal on Computing 0(0). source

Video Content Structuring⚓︎

Team⚓︎

  • Devine, Dennis J., and Jennifer L. Philips. "Do smarter teams do better: A meta-analysis of cognitive ability and team performance." Small group research 32.5 (2001): 507-532. source
  • Kozlowski, Steve WJ, and Daniel R. Ilgen. "Enhancing the effectiveness of work groups and teams." Psychological science in the public interest 7.3 (2006): 77-124. source
  • Wang, Xinyu, Zhou Zhao, and Wilfred Ng. "A comparative study of team formation in social networks." International conference on database systems for advanced applications. Springer, Cham, 2015. source
  • Andrejczuk, Ewa, et al. "The composition and formation of effective teams: computer science meets organizational psychology." The Knowledge Engineering Review 33 (2018). source
  • Gómez-Zará, Diego, Leslie A. DeChurch, and Noshir S. Contractor. "A taxonomy of team-assembly systems: Understanding how people use technologies to form teams." Proceedings of the ACM on Human-Computer Interaction 4.CSCW2 (2020): 1-36. source
  • Juárez, Julio, Cipriano Santos, and Carlos A. Brizuela. "A Comprehensive Review and a Taxonomy Proposal of Team Formation Problems." ACM Computing Surveys (CSUR) 54.7 (2021): 1-33. source

User Behavior⚓︎

Mobile⚓︎

  • Shaohui Wu, Yong Tan, Yubo Chen, Yitian (Sky) Liang (2022) How Is Mobile User Behavior Different?—A Hidden Markov Model of Cross-Mobile Application Usage Dynamics. Information Systems Research 0(0) source
  • Raluca M. Ursu, Qianyun Zhang, Elisabeth Honka (2022) Search Gaps and Consumer Fatigue. Marketing Science 0(0). source

Behavior Change⚓︎

  • Merz, M., & Steinherr, V. M. (2022). Process-based Guidance for Designing Behavior Change Support Systems: Marrying the Persuasive Systems Design Model to the Transtheoretical Model of Behavior Change. Communications of the Association for Information Systems, 50, pp-pp. source

Pricing⚓︎

  • Jinzhi Bu, David Simchi-Levi, Li Wang (2022) Offline Pricing and Demand Learning with Censored Data. Management Science 0(0). source
  • Wen Chen, Ying He, Saurabh Bansal (2023) Customized Dynamic Pricing When Customers Develop a Habit or Satiation. Operations Research 0(0). https://pubsonline.informs.org/doi/abs/10.1287/opre.2022.2412

Dynamic Pricing⚓︎

  • N. Bora Keskin, Yuexing Li, Jing-Sheng Song (2022) Data-Driven Dynamic Pricing and Ordering with Perishable Inventory in a Changing Environment. Management Science 0(0). source
  • Jinzhi Bu, David Simchi-Levi, Yunzong Xu (2022) Online Pricing with Offline Data: Phase Transition and Inverse Square Law. Management Science 0(0). source

Auditing⚓︎

  • Bouayad, Lina, Balaji Padmanabhan, and Kaushal Chari. "Audit Policies Under the Sentinel Effect: Deterrence-Driven Algorithms." Information Systems Research 30.2 (2019): 466-485. source

Reliable Prediction⚓︎

  • Romano, Yaniv, Evan Patterson, and Emmanuel Candes. "Conformalized quantile regression." Advances in neural information processing systems 32 (2019). source code
  • Sesia, Matteo, and Emmanuel J. Candès. "A comparison of some conformal quantile regression methods." Stat 9.1 (2020): e261. source
  • Model Agnostic Prediction Interval Estimator (MAPIE) is a python toolkit for prediction interval estimation.
  • Nam Ho-Nguyen, Fatma Kılınç-Karzan (2022) Risk Guarantees for End-to-End Prediction and Optimization Processes. Management Science 0(0). source

Online Platforms⚓︎

  • Nicole Immorlica, Brendan Lucier, Vahideh Manshadi, Alexander Wei (2022) Designing Approximately Optimal Search on Matching Platforms. Management Science 0(0). source

Advertising⚓︎

  • Ranjit M. Christopher, Sungho Park, Sang Pil Han, Min-Kyu Kim (2022) Bypassing Performance Optimizers of Real Time Bidding Systems in Display Ad Valuation. Information Systems Research 0(0). source
  • Jessica Clark, Jean-François Paiement, Foster Provost (2023) Who’s Watching TV?. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1204

Artifact Generalization⚓︎

  • Manoj A. Thomas , Yan Li , Allen S. Lee (2022) Generalizing the Information Systems Artifact. Information Systems Research 0(0). source

Healthcare⚓︎

  • John R. Birge, Ozan Candogan, Yiding Feng (2022) Controlling Epidemic Spread: Reducing Economic Losses with Targeted Closures. Management Science 0(0). source
  • Yu, Shuo; Chai, Yidong; Chen, Hsinchun; Sherman, Scott J.; and Brown, Randall A.. 2022. "Wearable Sensor-Based Chronic Condition Severity Assessment: An Adversarial Attention-Based Deep Multisource Multitask Learning Approach," MIS Quarterly, (46: 3) pp.1355-1394. source
  • Wanyi Chen, Nilay Tanik Argon, Tommy Bohrmann, Benjamin Linthicum, Kenneth Lopiano, Abhishek Mehrotra, Debbie Travers, Serhan Ziya (2022) Using Hospital Admission Predictions at Triage for Improving Patient Length of Stay in Emergency Departments. Operations Research 0(0). source
  • Shuo Yu, Yidong Chai, Sagar Samtani, Hongyan Liu, Hsinchun Chen (2023) Motion Sensor–Based Fall Prevention for Senior Care: A Hidden Markov Model with Generative Adversarial Network Approach. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1203
  • Matt Baucum, Anahita Khojandi, Rama Vasudevan, Ritesh Ramdhani (2023) Optimizing Patient-Specific Medication Regimen Policies Using Wearable Sensors in Parkinson’s Disease. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4747

Security⚓︎

  • Warut Khern-am-nuai, Matthew J. Hashim, Alain Pinsonneault, Weining Yang, Ninghui Li (2022) Augmenting Password Strength Meter Design Using the Elaboration Likelihood Model: Evidence from Randomized Experiments. Information Systems Research 0(0). source

Bot Detection⚓︎

  • Victor Benjamin, T. S. Raghu (2022) Augmenting Social Bot Detection with Crowd-Generated Labels. Information Systems Research 0(0). source

Inventory Management⚓︎

  • Meng Qi, Yuanyuan Shi, Yongzhi Qi, Chenxin Ma, Rong Yuan, Di Wu, Zuo-Jun (Max) Shen (2022) A Practical End-to-End Inventory Management Model with Deep Learning. Management Science 0(0). source

Auction⚓︎

  • Benedikt Bünz, Benjamin Lubin, Sven Seuken (2022) Designing Core-Selecting Payment Rules: A Computational Search Approach. Information Systems Research 33(4):1157-1173. source

Fraud Detection⚓︎

  • Weinmann, Markus; Valacich, Joseph; Schneider, Christoph; Jenkins, Jeffrey L.; and Hibbeln, Martin. 2022. "The Path of the Righteous: Using Trace Data to Understand Fraud Decisions in Real Time (Open Access)," MIS Quarterly, (46: 4) pp.2317-2336. source

Retail⚓︎

  • Junyu Cao, Wei Qi (2022) Stall Economy: The Value of Mobility in Retail on Wheels. Operations Research 0(0). source

Matching⚓︎

  • Yiding Feng, Rad Niazadeh, Amin Saberi (2023) Two-Stage Stochastic Matching and Pricing with Applications to Ride Hailing. Operations Research 0(0). https://doi.org/10.1287/opre.2022.2398

Response Prediction⚓︎

  • Gang Chen, Shuaiyong Xiao, Chenghong Zhang, Huimin Zhao (2023) A Theory-Driven Deep Learning Method for Voice Chat–Based Customer Response Prediction. Information Systems Research 0(0). https://doi.org/10.1287/isre.2022.1196

Risk Prediction⚓︎

  • Yang, Yi, et al. "Unlocking the Power of Voice for Financial Risk Prediction: A Theory-Driven Deep Learning Design Approach." Management Information Systems Quarterly 47.1 (2023): 63-96. https://aisel.aisnet.org/misq/vol47/iss1/5

Online Reviews⚓︎

  • Yu, Yifan, et al. "Unifying Algorithmic and Theoretical Perspectives: Emotions in Online Reviews and Sales."Management Information Systems Quarterly 47.1 (2023): 127-160. https://aisel.aisnet.org/misq/vol47/iss1/7
  • Yang, Mingwen, et al. "Responding to Online Reviews in Competitive Markets: A Controlled Diffusion Approach." Management Information Systems Quarterly 47.1 (2023): 161-194. https://aisel.aisnet.org/misq/vol47/iss1/8

Product Design⚓︎

  • Alex Burnap, John R. Hauser, Artem Timoshenko (2023) Product Aesthetic Design: A Machine Learning Augmentation. Marketing Science 0(0). https://doi.org/10.1287/mksc.2022.1429

© License⚓︎

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Last Update On 2023-04-15.

Copyright © 2019 - 2023 Yihong Liu
\ No newline at end of file + Technical Model / Design Science - MIS Reading List

Technical Model / Design Science⚓︎

On Design Science⚓︎

  • Hevner, Alan R., et al. "Design science in information systems research." MIS quarterly (2004): 75-105. source
  • Peffers, Ken, et al. "A design science research methodology for information systems research." Journal of management information systems 24.3 (2007): 45-77. source
  • Hevner, Alan, et al. "Design science research in information systems." Design research in information systems: theory and practice (2010): 9-22. source
  • Sein, Maung K., et al. "Action design research." MIS quarterly (2011): 37-56. source
  • Gregor, Shirley and Hevner, Alan R.. 2013. "Positioning and Presenting Design Science Research for Maximum Impact," MIS Quarterly, (37: 2) pp.337-355. source
  • Deng, Qi and Ji, Shaobo (2018) "A Review of Design Science Research in Information Systems: Concept, Process, Outcome, and Evaluation," Pacific Asia Journal of the Association for Information Systems: Vol. 10: Iss. 1, Article 2. source
  • Baskerville, Richard, et al. "Design science research contributions: Finding a balance between artifact and theory." Journal of the Association for Information Systems 19.5 (2018): 3. source
  • Maedche, Alexander, et al. "Conceptualization of the problem space in design science research." International conference on design science research in information systems and technology. Springer, Cham, 2019. source
  • Brendel, A. B., & Muntermann, J. (2022). Replication of design theories: Reflections on function, outcome, and impact. Information Systems Journal, 1– 19. source
  • Nagle, T., Doyle, C., Alhassan, I. M., & Sammon, D. (2022). The Research Method we Need or Deserve? A Literature Review of the Design Science Research Landscape. Communications of the Association for Information Systems, 50, pp-pp. source

Artificial Intelligence⚓︎

  • Nguyen, Q. N., Sidorova, A., & Torres, R. (2022). Artificial Intelligence in Business: A Literature Review and Research Agenda. Communications of the Association for Information Systems, 50, pp-pp. source

Deep Learning⚓︎

  • Luyang Chen, Markus Pelger, Jason Zhu (2023) Deep Learning in Asset Pricing. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4695
  • Samtani, S., Zhu, H., Padmanabhan, B., Chai, Y., & Chen, H. (2023). Deep learning for information systems research. Journal of Management Information Systems. https://doi.org/10.1080/07421222.2023.2172772

Reinforcement Learning⚓︎

  • Kaelbling, Leslie Pack, Michael L. Littman, and Andrew W. Moore. "Reinforcement learning: A survey." Journal of artificial intelligence research 4 (1996): 237-285. source
  • Arulkumaran, Kai, et al. "Deep reinforcement learning: A brief survey." IEEE Signal Processing Magazine 34.6 (2017): 26-38. source
  • Li, Yuxi. "Deep reinforcement learning." arXiv preprint arXiv:1810.06339 (2018). source
  • Li, Yuxi. "Reinforcement learning applications." arXiv preprint arXiv:1908.06973 (2019). source
  • Liebman, Elad, Maytal Saar-Tsechansky, and Peter Stone. "The Right Music at the Right Time: Adaptive Personalized Playlists Based on Sequence Modeling." MIS Quarterly 43.3 (2019). source
  • Wang, Hao-nan, et al. "Deep reinforcement learning: a survey." Frontiers of Information Technology & Electronic Engineering (2020): 1-19. source
  • Parker-Holder, Jack, et al. "Automated Reinforcement Learning (AutoRL): A Survey and Open Problems." arXiv preprint arXiv:2201.03916 (2022). source
  • Mark Sellke, Aleksandrs Slikvins (2022) The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity. Operations Research 0(0). source
  • Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu (2023) Nonstationary Reinforcement Learning: The Blessing of (More) Optimism. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4704
  • Yi Zhu, Jing Dong, Henry Lam (2023) Uncertainty Quantification and Exploration for Reinforcement Learning. Operations Research 0(0). https://doi.org/10.1287/opre.2023.2436

Self-Supervised Learning⚓︎

  • Jing, Longlong, and Yingli Tian. "Self-supervised visual feature learning with deep neural networks: A survey." IEEE transactions on pattern analysis and machine intelligence 43.11 (2020): 4037-4058. source
  • Xie, Yaochen, et al. "Self-supervised learning of graph neural networks: A unified review." arXiv preprint arXiv:2102.10757 (2021). source
  • Liu, Yixin, et al. "Graph self-supervised learning: A survey." arXiv preprint arXiv:2103.00111 (2021). source
  • Jaiswal, Ashish, et al. "A survey on contrastive self-supervised learning." Technologies 9.1 (2021): 2. source
  • Liu, Xiao, et al. "Self-supervised learning: Generative or contrastive." IEEE Transactions on Knowledge and Data Engineering (2021). source

Transfer Learning⚓︎

  • Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359. source
  • Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. "A survey of transfer learning." Journal of Big data 3.1 (2016): 1-40. source
  • Tan, Chuanqi, et al. "A survey on deep transfer learning." International conference on artificial neural networks. Springer, Cham, 2018. source
  • Zhuang, Fuzhen, et al. "A comprehensive survey on transfer learning." Proceedings of the IEEE 109.1 (2020): 43-76. source

Differential Privacy⚓︎

  • Dwork, Cynthia, et al. "Calibrating noise to sensitivity in private data analysis." Theory of cryptography conference. Springer, Berlin, Heidelberg, 2006. source
  • Zheng, Qinqing, et al. "Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion." arXiv preprint arXiv:2003.04493 (2020). source
  • Goodfellow, Ian. "Efficient per-example gradient computations." arXiv preprint arXiv:1510.01799 (2015). source
  • Abadi, Martin, et al. "Deep learning with differential privacy." Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. 2016. source
  • Mironov, Ilya. "Rényi differential privacy." 2017 IEEE 30th Computer Security Foundations Symposium (CSF). IEEE, 2017. source
  • McMahan, H. Brendan, et al. "A general approach to adding differential privacy to iterative training procedures." arXiv preprint arXiv:1812.06210 (2018). source
  • Mironov, Ilya, Kunal Talwar, and Li Zhang. "Rényi Differential Privacy of the Sampled Gaussian Mechanism." arXiv preprint arXiv:1908.10530 (2019). source
  • Dwork, Cynthia, and Aaron Roth. "The algorithmic foundations of differential privacy." Foundations and Trends in Theoretical Computer Science 9.3-4 (2014): 211-407. source
  • Dwork, Cynthia, and Adam Smith. "Differential privacy for statistics: What we know and what we want to learn." Journal of Privacy and Confidentiality 1.2 (2010). source
  • Ji, Zhanglong, Zachary C. Lipton, and Charles Elkan. "Differential privacy and machine learning: a survey and review." arXiv preprint arXiv:1412.7584 (2014). source
  • Jiang, Honglu, et al. "Differential Privacy and Its Applications in Social Network Analysis: A Survey." arXiv preprint arXiv:2010.02973 (2020). source
  • Yang, Mengmeng, et al. "Local differential privacy and its applications: A comprehensive survey." arXiv preprint arXiv:2008.03686 (2020). source

Explainable ML / DL / AI⚓︎

  • Angelino, Elaine, et al. "Learning certifiably optimal rule lists for categorical data." arXiv preprint arXiv:1704.01701 (2017). source
  • Lundberg, Scott M., and Su-In Lee. "A unified approach to interpreting model predictions." Advances in neural information processing systems 30 (2017). source
  • Lipton, Zachary C. "The mythos of model interpretability." Queue 16.3 (2018): 31-57. source
  • Lundberg, Scott M., et al. "From local explanations to global understanding with explainable AI for trees." Nature machine intelligence 2.1 (2020): 56-67. source
  • Molnar, Christoph. Interpretable machine learning. 2020. source
  • Arrieta, Alejandro Barredo, et al. "Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI." Information Fusion 58 (2020): 82-115. source
  • Wang, Zhuo, et al. "Scalable Rule-Based Representation Learning for Interpretable Classification." arXiv preprint arXiv:2109.15103 (2021). source
  • Chen, Valerie, et al. "Interpretable machine learning: Moving from mythos to diagnostics." Communications of the ACM, August 2022, Vol. 65 No. 8, Pages 43-50. source

Fairness⚓︎

  • Aumüller, Martin, Rasmus Pagh, and Francesco Silvestri. "Fair near neighbor search: Independent range sampling in high dimensions." Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems. 2020. source
  • Krakovsky, Marina. "## Formalizing Fairness." Communications of the ACM, August 2022, Vol. 65 No. 8, Pages 11-13. source
  • Dong, Yushun, et al. "Fairness in Graph Mining: A Survey." arXiv preprint arXiv:2204.09888 (2022). source

Active Learning⚓︎

  • Aggarwal, C. C., Kong, X., Gu, Q., Han, J., & Yu, P. S. (2014). "Active learning: A survey". In Data Classification: Algorithms and Applications (pp. 571-605). CRC Press. source
  • Ren, Pengzhen, et al. "A Survey of Deep Active Learning." ArXiv:2009.00236 [Cs, Stat], Aug. 2020. arXiv.org. source
  • Atahan, Pelin, and Sumit Sarkar. "Accelerated learning of user profiles." Management Science 57.2 (2011): 215-239. source

Label Imbalance⚓︎

  • Nasir, Murtaza, et al. "Improving Imbalanced Machine Learning with Neighborhood-Informed Synthetic Sample Placement." Journal of Management Information Systems 39.4 (2022): 1116-1145. https://doi.org/10.1080/07421222.2022.2127453

Label Noise⚓︎

  • Han, Bo, et al. "A survey of label-noise representation learning: Past, present and future." arXiv preprint arXiv:2011.04406 (2020). source

Natural Language Processing⚓︎

Text Summarization⚓︎

  • Rush, Alexander M., Sumit Chopra, and Jason Weston. "A neural attention model for abstractive sentence summarization." arXiv preprint arXiv:1509.00685 (2015). source
  • Chen, Yen-Chun, and Mohit Bansal. "Fast abstractive summarization with reinforce-selected sentence rewriting." arXiv preprint arXiv:1805.11080 (2018). source
  • Gehrmann, Sebastian, Yuntian Deng, and Alexander M. Rush. "Bottom-up abstractive summarization." arXiv preprint arXiv:1808.10792 (2018). source

Topic Modeling⚓︎

  • Jelodar, Hamed, et al. "Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey." Multimedia Tools and Applications 78.11 (2019): 15169-15211. source
  • Qiang, Jipeng, et al. "Short text topic modeling techniques, applications, and performance: a survey." IEEE Transactions on Knowledge and Data Engineering (2020). source
  • Vayansky, Ike, and Sathish AP Kumar. "A review of topic modeling methods." Information Systems 94 (2020): 101582. source
  • Kherwa, Pooja, and Poonam Bansal. "Topic modeling: a comprehensive review." EAI Endorsed transactions on scalable information systems 7.24 (2020). source
  • Chauhan, Uttam, and Apurva Shah. "Topic Modeling Using Latent Dirichlet allocation: A Survey." ACM Computing Surveys (CSUR) 54.7 (2021): 1-35. source
  • Yi Yang, Kunpeng Zhang, Yangyang Fan (2022) sDTM: A Supervised Bayesian Deep Topic Model for Text Analytics. Information Systems Research 0(0). source
  • Li, Weifeng and Chen, Hsinchun. 2022. "Discovering Emerging Threats in the Hacker Community: A Nonparametric Emerging Topic Detection Framework," MIS Quarterly, (46: 4) pp.2337-2350. source

Personalized Feedback⚓︎

  • Jiyeon Hong, Paul R. Hoban (2022) Writing More Compelling Creative Appeals: A Deep Learning-Based Approach. Marketing Science 0(0). source

Sentiment Analysis⚓︎

  • Rocklage, M. D., He, S., Rucker, D. D., & Nordgren, L. F. (2023). Beyond Sentiment: The Value and Measurement of Consumer Certainty in Language. Journal of Marketing Research, 0(0). https://doi.org/10.1177/00222437221134802

Decentralized Learning⚓︎

  • Li, Tian, et al. "Federated learning: Challenges, methods, and future directions." IEEE Signal Processing Magazine 37.3 (2020): 50-60. source
  • Lim, Wei Yang Bryan, et al. "Federated learning in mobile edge networks: A comprehensive survey." IEEE Communications Surveys & Tutorials 22.3 (2020): 2031-2063. source
  • Mothukuri, Viraaji, et al. "A survey on security and privacy of federated learning." Future Generation Computer Systems 115 (2021): 619-640. source
  • Kairouz, Peter, et al. "Advances and open problems in federated learning." Foundations and Trends® in Machine Learning 14.1–2 (2021): 1-210. source
  • Warnat-Herresthal, Stefanie, et al. "Swarm learning for decentralized and confidential clinical machine learning." Nature 594.7862 (2021): 265-270. source code
  • Kallista Bonawitz, et al. 2022. Federated learning and privacy. Commun. ACM 65, 4 (April 2022), 90–97. source

Personality Measurement⚓︎

  • Kai Yang, Raymond Y. K. Lau, Ahmed Abbasi (2022) Getting Personal: A Deep Learning Artifact for Text-Based Measurement of Personality. Information Systems Research 0(0). source

Adversaries⚓︎

  • Li, Weifeng, and Yidong Chai. "Assessing and Enhancing Adversarial Robustness of Predictive Analytics: An Empirically Tested Design Framework." Journal of Management Information Systems 39.2 (2022): 542-572. source

Data Imputation⚓︎

  • Lin, Wei-Chao, and Chih-Fong Tsai. "Missing value imputation: a review and analysis of the literature (2006–2017)." Artificial Intelligence Review 53.2 (2020): 1487-1509. source
  • Hasan, Md Kamrul, et al. "Missing value imputation affects the performance of machine learning: A review and analysis of the literature (2010–2021)." Informatics in Medicine Unlocked 27 (2021): 100799. source

Application⚓︎

  • Aiken, Emily, et al. "Machine learning and phone data can improve targeting of humanitarian aid." Nature (2022): 1-7. source
  • Nan Zhang, Heng Xu (2023) Fairness of Ratemaking for Catastrophe Insurance: Lessons from Machine Learning. Information Systems Research 0(0). https://doi.org/10.1287/isre.2022.1195
  • Arindam Ray, Wolfgang Jank, Kaushik Dutta, Matthew Mullarkey (2023) An LSTM+ Model for Managing Epidemics: Using Population Mobility and Vulnerability for Forecasting COVID-19 Hospital Admissions. INFORMS Journal on Computing 0(0). https://doi.org/10.1287/ijoc.2023.1269

Conversational Agents⚓︎

  • Elshan, E., Ebel, P., Söllner, M., & Leimeister, J. M. (2023). Leveraging Low Code Development of Smart Personal Assistants: An Integrated Design Approach with the SPADE Method. Journal of Management Information Systems, 40(1), 96-129. https://doi.org/10.1080/07421222.2023.2172776

Transparency⚓︎

  • Bitzer, T., Wiener, M., & Cram, W. (2023). Algorithmic Transparency: Concepts, Antecedents, and Consequences – A Review and Research Framework. Communications of the Association for Information Systems, 52, pp-pp. https://aisel.aisnet.org/cais/vol52/iss1/16

Graph And Network⚓︎

Graph Neural Network⚓︎

  • Kipf, T. N. "Deep learning with graph-structured representations." (2020). pdf
  • Wu, Zonghan, et al. "A comprehensive survey on graph neural networks." IEEE Transactions on Neural Networks and Learning Systems (2020). source
  • Zhou, Jie, et al. "Graph neural networks: A review of methods and applications." arXiv preprint arXiv:1812.08434 (2018). source
  • Zhang, Chuxu, et al. "Heterogeneous graph neural network." Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019. source
  • Wang, Xiao, et al. "Heterogeneous graph attention network." The World Wide Web Conference. 2019. source
  • Hu, Ziniu, et al. "Heterogeneous graph transformer." Proceedings of The Web Conference 2020. 2020. source

Graph Embedding⚓︎

  • Goyal, Palash, and Emilio Ferrara. "Graph embedding techniques, applications, and performance: A survey." Knowledge-Based Systems 151 (2018): 78-94. source
  • Xi Chen, Yan Liu, Cheng Zhang (2022) Distinguishing Homophily from Peer Influence Through Network Representation Learning. INFORMS Journal on Computing 0(0). source

Graphical Causality⚓︎

  • Bernhard Schölkopf, et al. "Towards Causal Representation Learning." (2021). source

Influence Maximization⚓︎

  • Li, Yuchen, et al. "Influence maximization on social graphs: A survey." IEEE Transactions on Knowledge and Data Engineering 30.10 (2018): 1852-1872. source
  • Banerjee, Suman, Mamata Jenamani, and Dilip Kumar Pratihar. "A survey on influence maximization in a social network." Knowledge and Information Systems 62.9 (2020): 3417-3455. source
  • De Nittis, Giuseppe, and Nicola Gatti. "How to maximize the spread of social influence: A survey." arXiv preprint arXiv:1806.07757 (2018). source
  • Ozan Candogan (2022) Persuasion in Networks: Public Signals and Cores. Operations Research 0(0). source

Vertical Markets⚓︎

  • Soheil Ghili (2022) Network Formation and Bargaining in Vertical Markets: The Case of Narrow Networks in Health Insurance. Marketing Science 0(0). source

Network Structures⚓︎

  • Sinan Aral, Paramveer S. Dhillon (2022) What (Exactly) Is Novelty in Networks? Unpacking the Vision Advantages of Brokers, Bridges, and Weak Ties. Management Science 0(0). source
  • Schecter, Aaron, Omid Nohadani, and Noshir Contractor. "A Robust Inference Method for Decision Making in Networks." Management Information Systems Quarterly 46.2 (2022): 713-738. source
  • Syngjoo Choi, Sanjeev Goyal, Frederic Moisan, Yu Yang Tony To (2023) Learning in Networks: An Experiment on Large Networks with Real-World Features. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4680

Network Privacy⚓︎

  • Marcella Hastings, Brett Hemenway Falk, Gerry Tsoukalas (2022) Privacy-Preserving Network Analytics. Management Science 0(0). source

Recommendation Systems⚓︎

Recommendation Objectives⚓︎

  • Gunawardana, Asela, and Guy Shani. "A survey of accuracy evaluation metrics of recommendation tasks." Journal of Machine Learning Research 10.12 (2009). source
  • Kunaver, Matevž, and Tomaž Požrl. "Diversity in recommender systems–A survey." Knowledge-based systems 123 (2017): 154-162. source
  • Kaminskas, Marius, and Derek Bridge. "Diversity, serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems." ACM Transactions on Interactive Intelligent Systems (TiiS) 7.1 (2016): 1-42. source
  • Wu, Qiong, et al. "Recent advances in diversified recommendation." arXiv preprint arXiv:1905.06589 (2019). source
  • Wu, Le, et al. "A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation." IEEE Transactions on Knowledge and Data Engineering (2022). source
  • Alhijawi, Bushra, Arafat Awajan, and Salam Fraihat. "Survey on the Objectives of Recommender System: Measures, Solutions, Evaluation Methodology, and New Perspectives." ACM Computing Surveys (CSUR) (2022). source

Dataset⚓︎

  • Gao, Chongming, et al. "KuaiRec: A Fully-observed Dataset for Recommender Systems." arXiv preprint arXiv:2202.10842 (2022). source web
  • Chin, Jin Yao, Yile Chen, and Gao Cong. "The Datasets Dilemma: How Much Do We Really Know About Recommendation Datasets?." Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. 2022. source
  • Lü, Linyuan, and Tao Zhou. "Link prediction in complex networks: A survey." Physica A: statistical mechanics and its applications 390.6 (2011): 1150-1170. source
  • Lü, Linyuan, and Tao Zhou. "Link prediction in complex networks: A survey." Physica A: statistical mechanics and its applications 390.6 (2011): 1150-1170. https://doi.org/10.1145/3012704
  • Wang, P., Xu, B., Wu, Y. et al. Link prediction in social networks: the state-of-the-art. Sci. China Inf. Sci. 58, 1–38 (2015). https://doi.org/10.1007/s11432-014-5237-y
  • Kim, J., Diesner, J. Formational bounds of link prediction in collaboration networks. Scientometrics 119, 687–706 (2019). https://doi.org/10.1007/s11192-019-03055-6
  • Kumar, Ajay, et al. "Link prediction techniques, applications, and performance: A survey." Physica A: Statistical Mechanics and its Applications 553 (2020): 124289. source
  • Qin, Meng, and Dit-Yan Yeung. "Temporal Link Prediction: A Unified Framework, Taxonomy, and Review." arXiv preprint arXiv:2210.08765 (2022). https://doi.org/10.48550/arXiv.2210.08765
  • Wu, H., Song, C., Ge, Y. et al. Link Prediction on Complex Networks: An Experimental Survey. Data Sci. Eng. 7, 253–278 (2022). https://doi.org/10.1007/s41019-022-00188-2

Recommendation Framework⚓︎

  • Anelli, Vito Walter, et al. "Elliot: a comprehensive and rigorous framework for reproducible recommender systems evaluation." Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2021. source code
  • TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale recommender systems (RecSys). It allows authors to train models with large embedding tables sharded across many GPUs.
  • Spotlight uses PyTorch to build both deep and shallow recommender models. By providing both a slew of building blocks for loss functions (various pointwise and pairwise ranking losses), representations (shallow factorization representations, deep sequence models), and utilities for fetching (or generating) recommendation datasets, it aims to be a tool for rapid exploration and prototyping of new recommender models.
  • MTReclib provides a PyTorch implementation of multi-task recommendation models and common datasets.
  • The repository microsoft/recommenders contains examples and best practices for building recommendation systems, provided as Jupyter notebooks.
  • Surprise is a Python scikit for building and analyzing recommender systems that deal with explicit rating data.
  • The repository hiroyuki-kasai/NMFLibrary is a pure-Matlab library of a collection of algorithms of non-negative matrix factorization (NMF).
  • QMF is a fast and scalable C++ library for implicit-feedback matrix factorization models (WALS and BPR).
  • The repository benfred/implicit provides fast Python implementations of several different popular recommendation algorithms for implicit feedback datasets.
  • This repository liu-yihong/BPRH implements the Bayesian personalized ranking method for heterogeneous implicit feedback.
  • reXmeX is recommender system evaluation metric library. It consists of utilities for recommender system evaluation. First, it provides a comprehensive collection of metrics for the evaluation of recommender systems. Second, it includes a variety of methods for reporting and plotting the performance results. Implemented metrics cover a range of well-known metrics and newly proposed metrics from data mining conferences and prominent journals.

Sequential Recommendation Systems⚓︎

  • Quadrana, Massimo, Paolo Cremonesi, and Dietmar Jannach. "Sequence-aware recommender systems." ACM Computing Surveys (CSUR) 51.4 (2018): 1-36. source
  • Maher, Mohamed, et al. "Comprehensive Empirical Evaluation of Deep Learning Approaches for Session-based Recommendation in E-Commerce." arXiv preprint arXiv:2010.12540 (2020). source
  • Fang, Hui, et al. "Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations." ACM Transactions on Information Systems (TOIS) 39.1 (2020): 1-42. source
  • Latifi, Sara, Noemi Mauro, and Dietmar Jannach. "Session-aware recommendation: A surprising quest for the state-of-the-art." Information Sciences 573 (2021): 291-315. source
  • Wang, Shoujin, et al. "A survey on session-based recommender systems." ACM Computing Surveys (CSUR) 54.7 (2021): 1-38. source
  • Wen Wang, Beibei Li, Xueming Luo, Xiaoyi Wang (2022) Deep Reinforcement Learning for Sequential Targeting. Management Science 0(0). source
  • Omid Rafieian (2022) Optimizing User Engagement Through Adaptive Ad Sequencing. Marketing Science 0(0). source
  • Yifu Li, Christopher Thomas Ryan, Lifei Sheng (2023) Optimal Sequencing in Single-Player Games. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4665
  • Marios Kokkodis, Panagiotis G. Ipeirotis (2023) The Good, the Bad, and the Unhirable: Recommending Job Applicants in Online Labor Markets. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4690

User-Item Matrix Factorization⚓︎

  • Su, Xiaoyuan, and Taghi M. Khoshgoftaar. "A survey of collaborative filtering techniques." Advances in artificial intelligence 2009 (2009). source
  • Cacheda, Fidel, et al. "Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems." ACM Transactions on the Web (TWEB) 5.1 (2011): 1-33. source
  • Shi, Yue, Martha Larson, and Alan Hanjalic. "Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges." ACM Computing Surveys (CSUR) 47.1 (2014): 1-45. source
  • Han, Soyeon Caren, et al. "GLocal-K: Global and Local Kernels for Recommender Systems." Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2021. source
  • Rendle, Steffen, et al. "Neural collaborative filtering vs. matrix factorization revisited." Fourteenth ACM conference on recommender systems. 2020. source

Graph Neural Network Based Recommendation⚓︎

  • Wu, Shiwen, et al. "Graph neural networks in recommender systems: a survey." arXiv preprint arXiv:2011.02260 (2020). source

Reinforcement Learning Based Recommendation⚓︎

  • Lin, Yuanguo, et al. "A Survey on Reinforcement Learning for Recommender Systems." arXiv preprint arXiv:2109.10665 (2021). source

Causal Learning⚓︎

  • Si, Zihua et al. “A Model-Agnostic Causal Learning Framework for Recommendation using Search Data.” (2022). source code

Self-Supervised Learning⚓︎

  • Yu, Junliang, et al. "Self-Supervised Learning for Recommender Systems: A Survey." arXiv preprint arXiv:2203.15876 (2022). source

Debias⚓︎

  • Schnabel, Tobias, et al. "Recommendations as treatments: Debiasing learning and evaluation." international conference on machine learning. PMLR, 2016. source
  • Chen, Jiawei, et al. "AutoDebias: Learning to Debias for Recommendation." arXiv preprint arXiv:2105.04170 (2021). source
  • Jiawei Chen on github.com provides a repository at jiawei-chen/RecDebiasing

User Reviews for Recommendation⚓︎

  • Sachdeva, Noveen, and Julian McAuley. "How useful are reviews for recommendation? a critical review and potential improvements." Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020. source

Regulations⚓︎

  • Tommaso Di Noia, et al. 2022. Recommender systems under European AI regulations. Commun. ACM 65, 4 (April 2022), 69–73. source

Healthcare⚓︎

  • Ali Hajjar, Oguzhan Alagoz (2022) Personalized Disease Screening Decisions Considering a Chronic Condition. Management Science 0(0). source
  • Xiang Hui, Zekun Liu, Weiqing Zhang (2023) From High Bar to Uneven Bars: The Impact of Information Granularity in Quality Certification. Management Science 0(0). https://pubsonline.informs.org/doi/abs/10.1287/isre.2022.1191
  • Josh C. D’Aeth, Shubhechyya Ghosal, Fiona Grimm, David Haw, Esma Koca, Krystal Lau, Huikang Liu, Stefano Moret, Dheeya Rizmie, Peter C. Smith, Giovanni Forchini, Marisa Miraldo, Wolfram Wiesemann (2023) Optimal Hospital Care Scheduling During the SARS-CoV-2 Pandemic. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4679
  • Johnson, M., Murthy, D., Robertson, B. W., Smith, W. R., & Stephens, K. K. (2023). Moving Emergency Response Forward: Leveraging Machine-Learning Classification of Disaster-Related Images Posted on Social Media. Journal of Management Information Systems, 40(1), 163-182. https://doi.org/10.1080/07421222.2023.2172778

Point-of-Interest⚓︎

  • Xiao-Jun Wang, Tao Liu, Weiguo Fan (2023) TGVx: Dynamic Personalized POI Deep Recommendation Model. INFORMS Journal on Computing 0(0). https://doi.org/10.1287/ijoc.2023.1286

Explainable Recommendation⚓︎

  • Zhang, Yongfeng, and Xu Chen. "Explainable recommendation: A survey and new perspectives." Foundations and Trends in Information Retrieval 14.1 (2020): 1-101. source
  • Chen, Xu, Yongfeng Zhang, and Ji-Rong Wen. "Measuring" Why" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendation." arXiv preprint arXiv:2202.06466 (2022). source

Attacking Recommendation Systems⚓︎

  • Su, Xue-Feng, Hua-Jun Zeng, and Zheng Chen. "Finding group shilling in recommendation system." Special interest tracks and posters of the 14th international conference on World Wide Web. 2005. source
  • O'Donovan, John, and Barry Smyth. "Is trust robust? An analysis of trust-based recommendation." Proceedings of the 11th international conference on Intelligent user interfaces. 2006. source
  • Hurley, Neil J., Michael P. O'Mahony, and Guenole CM Silvestre. "Attacking recommender systems: A cost-benefit analysis." IEEE Intelligent Systems 22.3 (2007): 64-68. source
  • Patel, Krupa, et al. "A state of art survey on shilling attack in collaborative filtering based recommendation system." Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 1. Springer, Cham, 2016. source
  • Fang, Minghong, et al. "Poisoning attacks to graph-based recommender systems." Proceedings of the 34th Annual Computer Security Applications Conference. 2018. source
  • Hu, Rui, et al. "Targeted poisoning attacks on social recommender systems." 2019 IEEE Global Communications Conference (GLOBECOM). IEEE, 2019. source
  • Zhang, Hengtong, et al. "Practical data poisoning attack against next-item recommendation." Proceedings of The Web Conference 2020. 2020. source
  • Song, Junshuai, et al. "Poisonrec: an adaptive data poisoning framework for attacking black-box recommender systems." 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 2020. source
  • Wu, Zih-Wun, Chiao-Ting Chen, and Szu-Hao Huang. "Poisoning attacks against knowledge graph-based recommendation systems using deep reinforcement learning." Neural Computing and Applications (2021): 1-19. source
  • Chen, Liang, et al. "Data poisoning attacks on neighborhood‐based recommender systems." Transactions on Emerging Telecommunications Technologies 32.6 (2021): e3872. source
  • Zhang, Hengtong, et al. "Data Poisoning Attack against Recommender System Using Incomplete and Perturbed Data." Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2021. source
  • Fan, Wenqi, et al. "Attacking Black-box Recommendations via Copying Cross-domain User Profiles." 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021. source

Diversity⚓︎

  • Kexin Yin, Xiao Fang, Bintong Chen, Olivia R. Liu Sheng (2022) Diversity Preference-Aware Link Recommendation for Online Social Networks. Information Systems Research 0(0). source

Multi-Sided⚓︎

  • Rastegari, Baharak, et al. "Two-sided matching with partial information." Proceedings of the fourteenth ACM conference on Electronic Commerce. 2013. https://doi.org/10.1145/2482540.2482607
  • Malgonde, Onkar, et al. "TAMING COMPLEXITY IN SEARCH MATCHING: TWO-SIDED RECOMMENDER SYSTEMS ON DIGITAL PLATFORMS." Mis Quarterly 44.1 (2020). https://aisel.aisnet.org/misq/vol44/iss1/5/
  • Malgonde, Onkar S., et al. "Managing Digital Platforms with Robust Multi-Sided Recommender Systems." Journal of Management Information Systems 39.4 (2022): 938-968. https://doi.org/10.1080/07421222.2022.2127440

Followee Recommendation⚓︎

  • Yaxuan Ran, Jiani Liu, Yishi Zhang (2023) Integrating Users’ Contextual Engagements with Their General Preferences: An Interpretable Followee Recommendation Method. INFORMS Journal on Computing 0(0). https://doi.org/10.1287/ijoc.2023.1284

Reference Learning⚓︎

  • Jiapeng Liu, Miłosz Kadziński, Xiuwu Liao (2023) Modeling Contingent Decision Behavior: A Bayesian Nonparametric Preference-Learning Approach. INFORMS Journal on Computing 0(0). https://doi.org/10.1287/ijoc.2023.1292

Operations Research⚓︎

Lagrangian Relaxation⚓︎

  • Fisher, Marshall L. "The Lagrangian relaxation method for solving integer programming problems." Management science 27.1 (1981): 1-18. source
  • Ghoshal, Abhijeet, et al. "Hiding Sensitive Information when Sharing Distributed Transactional Data." Information systems research 31.2 (2020): 473-490. source

Column Generation⚓︎

  • Menon, Syam, and Sumit Sarkar. "Privacy and Big Data: Scalable Approaches to Sanitize Large Transactional Databases for Sharing." MIS Quarterly 40.4 (2016). source
  • Dash, Sanjeeb, Oktay Günlük, and Dennis Wei. "Boolean decision rules via column generation." arXiv preprint arXiv:1805.09901 (2018). source

Decision Under Uncertainty⚓︎

  • Sen, Suvrajeet, and Julia L. Higle. "An introductory tutorial on stochastic linear programming models." Interfaces 29.2 (1999): 33-61. source
  • Alessio Trivella, Danial Mohseni-Taheri, Selvaprabu Nadarajah (2022) Meeting Corporate Renewable Power Targets. Management Science 0(0). source

Data-Driven Optimization⚓︎

  • Gah-Yi Ban, Cynthia Rudin (2018) The Big Data Newsvendor: Practical Insights from Machine Learning. Operations Research 67(1):90-108. source
  • L. Jeff Hong, Zhiyuan Huang, Henry Lam (2020) Learning-Based Robust Optimization: Procedures and Statistical Guarantees. Management Science 67(6):3447-3467. source
  • Dimitris Bertsimas, Nihal Koduri (2021) Data-Driven Optimization: A Reproducing Kernel Hilbert Space Approach. Operations Research 70(1):454-471. source
  • Keliang Wang, Leonardo Lozano, Carlos Cardonha, David Bergman (2023) Optimizing over an Ensemble of Trained Neural Networks. INFORMS Journal on Computing 0(0). https://doi.org/10.1287/ijoc.2023.1285
  • Omar Besbes, Omar Mouchtaki (2023) How Big Should Your Data Really Be? Data-Driven Newsvendor: Learning One Sample at a Time. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4725

Predict-then-Optimize Paradigm⚓︎

  • Bertsimas, D., & Kallus, N. (2018). From Predictive to Prescriptive Analytics. ArXiv:1402.5481. source
  • Demirovic, E., Stuckey, P. J., Bailey, J., Chan, J., Leckie, C., Ramamohanarao, K., & Guns, T. (2019). Predict+Optimise with Ranking Objectives: Exhaustively Learning Linear Functions. 1078–1085.
  • Elmachtoub, A. N., & Grigas, P. (2020). Smart “Predict, then Optimize.” ArXiv:1710.08005. source
  • Mandi, J., Bucarey, V., Mulamba, M., & Guns, T. (2022). Predict and Optimize: Through the Lens of Learning to Rank. ArXiv:2112.03609. source

Multi-Objective Optimization⚓︎

  • Arne Herzel, Stefan Ruzika, Clemens Thielen (2021) Approximation Methods for Multiobjective Optimization Problems: A Survey. INFORMS Journal on Computing 33(4):1284-1299. source

E-Commerce⚓︎

  • Maximilian Schiffer, Nils Boysen, Patrick S. Klein, Gilbert Laporte, Marco Pavone (2022) Optimal Picking Policies in E-Commerce Warehouses. Management Science 0(0). source
  • Hanwei Li, David Simchi-Levi, Michelle Xiao Wu, Weiming Zhu (2022) Estimating and Exploiting the Impact of Photo Layout: A Structural Approach. Management Science 0(0). source
  • Goldstein, Anat; Oestreicher-Singer, Gal; Barzilay, Ohad; and Yahav, Inbal. 2022. "Are We There Yet? Analyzing Progress in the Conversion Funnel Using the Diversity of Searched Products," MIS Quarterly, (46: 4) pp.2015-2054. source

Assortment Optimization⚓︎

  • Zhen-Yu Chen, Zhi-Ping Fan, Minghe Sun (2022) Machine Learning Methods for Data-Driven Demand Estimation and Assortment Planning Considering Cross-Selling and Substitutions. INFORMS Journal on Computing 0(0). source
  • Santiago R. Balseiro, Antoine Désir (2022) Incentive-Compatible Assortment Optimization for Sponsored Products. Management Science 0(0). source
  • Antoine Désir, Vineet Goyal, Bo Jiang, Tian Xie, Jiawei Zhang (2023) Robust Assortment Optimization Under the Markov Chain Choice Model. Operations Research 0(0). https://doi.org/10.1287/opre.2022.2420
  • Ningyuan Chen, Andre A. Cire, Ming Hu, Saman Lagzi (2023) Model-Free Assortment Pricing with Transaction Data. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4651

Decision Analysis⚓︎

  • Eric Neyman, Tim Roughgarden (2023) From Proper Scoring Rules to Max-Min Optimal Forecast Aggregation. Operations Research 0(0). source
  • Ibrahim Abada, Xavier Lambin (2023) Artificial Intelligence: Can Seemingly Collusive Outcomes Be Avoided?. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4623
  • Asa B. Palley, Ville A. Satopää (2023) Boosting the Wisdom of Crowds Within a Single Judgment Problem: Weighted Averaging Based on Peer Predictions. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4648

Electric Vehicle⚓︎

  • Wei Qi, Yuli Zhang, Ningwei Zhang (2023) Scaling Up Electric-Vehicle Battery Swapping Services in Cities: A Joint Location and Repairable-Inventory Model. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4731

Probabilistic Reasoning⚓︎

  • Li, Zhepeng, et al. "Utility-based link recommendation for online social networks." Management Science 63.6 (2017): 1938-1952. source
  • Ghoshal, Abhijeet, Syam Menon, and Sumit Sarkar. "Recommendations using information from multiple association rules: A probabilistic approach." Information Systems Research 26.3 (2015): 532-551. source

Agent-based Modeling & Simulation⚓︎

  • Bonabeau, Eric. "Agent-based modeling: Methods and techniques for simulating human systems." Proceedings of the national academy of sciences 99.suppl 3 (2002): 7280-7287. source
  • Macal, Charles M., and Michael J. North. "Tutorial on agent-based modeling and simulation." Proceedings of the Winter Simulation Conference, 2005.. IEEE, 2005. source
  • Railsback, Steven F., Steven L. Lytinen, and Stephen K. Jackson. "Agent-based simulation platforms: Review and development recommendations." Simulation 82.9 (2006): 609-623. source
  • An, Li. "Modeling human decisions in coupled human and natural systems: Review of agent-based models." Ecological Modelling 229 (2012): 25-36. source
  • Abar, Sameera, et al. "Agent Based Modelling and Simulation tools: A review of the state-of-art software." Computer Science Review 24 (2017): 13-33. source
  • Mladenov, Martin, et al. "RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems." arXiv preprint arXiv:2103.08057 (2021). source official webiste github
  • Dong, John Qi (2022) "Using Simulation in Information Systems Research," Journal of the Association for Information Systems, 23(2), 408-417. source
  • Zhaolin Hu, L. Jeff Hong (2022) Robust Simulation with Likelihood-Ratio Constrained Input Uncertainty. INFORMS Journal on Computing 0(0). source
  • Lucy E. Morgan, Luke Rhodes-Leader, Russell R. Barton (2022) Reducing and Calibrating for Input Model Bias in Computer Simulation. INFORMS Journal on Computing 0(0). source

Video Content Structuring⚓︎

Team⚓︎

  • Devine, Dennis J., and Jennifer L. Philips. "Do smarter teams do better: A meta-analysis of cognitive ability and team performance." Small group research 32.5 (2001): 507-532. source
  • Kozlowski, Steve WJ, and Daniel R. Ilgen. "Enhancing the effectiveness of work groups and teams." Psychological science in the public interest 7.3 (2006): 77-124. source
  • Wang, Xinyu, Zhou Zhao, and Wilfred Ng. "A comparative study of team formation in social networks." International conference on database systems for advanced applications. Springer, Cham, 2015. source
  • Andrejczuk, Ewa, et al. "The composition and formation of effective teams: computer science meets organizational psychology." The Knowledge Engineering Review 33 (2018). source
  • Gómez-Zará, Diego, Leslie A. DeChurch, and Noshir S. Contractor. "A taxonomy of team-assembly systems: Understanding how people use technologies to form teams." Proceedings of the ACM on Human-Computer Interaction 4.CSCW2 (2020): 1-36. source
  • Juárez, Julio, Cipriano Santos, and Carlos A. Brizuela. "A Comprehensive Review and a Taxonomy Proposal of Team Formation Problems." ACM Computing Surveys (CSUR) 54.7 (2021): 1-33. source

User Behavior⚓︎

Mobile⚓︎

  • Shaohui Wu, Yong Tan, Yubo Chen, Yitian (Sky) Liang (2022) How Is Mobile User Behavior Different?—A Hidden Markov Model of Cross-Mobile Application Usage Dynamics. Information Systems Research 0(0) source
  • Raluca M. Ursu, Qianyun Zhang, Elisabeth Honka (2022) Search Gaps and Consumer Fatigue. Marketing Science 0(0). source

Behavior Change⚓︎

  • Merz, M., & Steinherr, V. M. (2022). Process-based Guidance for Designing Behavior Change Support Systems: Marrying the Persuasive Systems Design Model to the Transtheoretical Model of Behavior Change. Communications of the Association for Information Systems, 50, pp-pp. source

Pricing⚓︎

  • Jinzhi Bu, David Simchi-Levi, Li Wang (2022) Offline Pricing and Demand Learning with Censored Data. Management Science 0(0). source
  • Wen Chen, Ying He, Saurabh Bansal (2023) Customized Dynamic Pricing When Customers Develop a Habit or Satiation. Operations Research 0(0). https://pubsonline.informs.org/doi/abs/10.1287/opre.2022.2412

Dynamic Pricing⚓︎

  • N. Bora Keskin, Yuexing Li, Jing-Sheng Song (2022) Data-Driven Dynamic Pricing and Ordering with Perishable Inventory in a Changing Environment. Management Science 0(0). source
  • Jinzhi Bu, David Simchi-Levi, Yunzong Xu (2022) Online Pricing with Offline Data: Phase Transition and Inverse Square Law. Management Science 0(0). source

Auditing⚓︎

  • Bouayad, Lina, Balaji Padmanabhan, and Kaushal Chari. "Audit Policies Under the Sentinel Effect: Deterrence-Driven Algorithms." Information Systems Research 30.2 (2019): 466-485. source

Reliable Prediction⚓︎

  • Romano, Yaniv, Evan Patterson, and Emmanuel Candes. "Conformalized quantile regression." Advances in neural information processing systems 32 (2019). source code
  • Sesia, Matteo, and Emmanuel J. Candès. "A comparison of some conformal quantile regression methods." Stat 9.1 (2020): e261. source
  • Model Agnostic Prediction Interval Estimator (MAPIE) is a python toolkit for prediction interval estimation.
  • Nam Ho-Nguyen, Fatma Kılınç-Karzan (2022) Risk Guarantees for End-to-End Prediction and Optimization Processes. Management Science 0(0). source

Online Platforms⚓︎

  • Nicole Immorlica, Brendan Lucier, Vahideh Manshadi, Alexander Wei (2022) Designing Approximately Optimal Search on Matching Platforms. Management Science 0(0). source

Advertising⚓︎

  • Ranjit M. Christopher, Sungho Park, Sang Pil Han, Min-Kyu Kim (2022) Bypassing Performance Optimizers of Real Time Bidding Systems in Display Ad Valuation. Information Systems Research 0(0). source
  • Jessica Clark, Jean-François Paiement, Foster Provost (2023) Who’s Watching TV?. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1204

Artifact Generalization⚓︎

  • Manoj A. Thomas , Yan Li , Allen S. Lee (2022) Generalizing the Information Systems Artifact. Information Systems Research 0(0). source

Healthcare⚓︎

  • John R. Birge, Ozan Candogan, Yiding Feng (2022) Controlling Epidemic Spread: Reducing Economic Losses with Targeted Closures. Management Science 0(0). source
  • Yu, Shuo; Chai, Yidong; Chen, Hsinchun; Sherman, Scott J.; and Brown, Randall A.. 2022. "Wearable Sensor-Based Chronic Condition Severity Assessment: An Adversarial Attention-Based Deep Multisource Multitask Learning Approach," MIS Quarterly, (46: 3) pp.1355-1394. source
  • Wanyi Chen, Nilay Tanik Argon, Tommy Bohrmann, Benjamin Linthicum, Kenneth Lopiano, Abhishek Mehrotra, Debbie Travers, Serhan Ziya (2022) Using Hospital Admission Predictions at Triage for Improving Patient Length of Stay in Emergency Departments. Operations Research 0(0). source
  • Shuo Yu, Yidong Chai, Sagar Samtani, Hongyan Liu, Hsinchun Chen (2023) Motion Sensor–Based Fall Prevention for Senior Care: A Hidden Markov Model with Generative Adversarial Network Approach. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1203
  • Matt Baucum, Anahita Khojandi, Rama Vasudevan, Ritesh Ramdhani (2023) Optimizing Patient-Specific Medication Regimen Policies Using Wearable Sensors in Parkinson’s Disease. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4747

Security⚓︎

  • Warut Khern-am-nuai, Matthew J. Hashim, Alain Pinsonneault, Weining Yang, Ninghui Li (2022) Augmenting Password Strength Meter Design Using the Elaboration Likelihood Model: Evidence from Randomized Experiments. Information Systems Research 0(0). source

Bot Detection⚓︎

  • Victor Benjamin, T. S. Raghu (2022) Augmenting Social Bot Detection with Crowd-Generated Labels. Information Systems Research 0(0). source

Inventory Management⚓︎

  • Meng Qi, Yuanyuan Shi, Yongzhi Qi, Chenxin Ma, Rong Yuan, Di Wu, Zuo-Jun (Max) Shen (2022) A Practical End-to-End Inventory Management Model with Deep Learning. Management Science 0(0). source

Auction⚓︎

  • Benedikt Bünz, Benjamin Lubin, Sven Seuken (2022) Designing Core-Selecting Payment Rules: A Computational Search Approach. Information Systems Research 33(4):1157-1173. source

Fraud Detection⚓︎

  • Weinmann, Markus; Valacich, Joseph; Schneider, Christoph; Jenkins, Jeffrey L.; and Hibbeln, Martin. 2022. "The Path of the Righteous: Using Trace Data to Understand Fraud Decisions in Real Time (Open Access)," MIS Quarterly, (46: 4) pp.2317-2336. source

Retail⚓︎

  • Junyu Cao, Wei Qi (2022) Stall Economy: The Value of Mobility in Retail on Wheels. Operations Research 0(0). source

Matching⚓︎

  • Yiding Feng, Rad Niazadeh, Amin Saberi (2023) Two-Stage Stochastic Matching and Pricing with Applications to Ride Hailing. Operations Research 0(0). https://doi.org/10.1287/opre.2022.2398

Response Prediction⚓︎

  • Gang Chen, Shuaiyong Xiao, Chenghong Zhang, Huimin Zhao (2023) A Theory-Driven Deep Learning Method for Voice Chat–Based Customer Response Prediction. Information Systems Research 0(0). https://doi.org/10.1287/isre.2022.1196

Risk Prediction⚓︎

  • Yang, Yi, et al. "Unlocking the Power of Voice for Financial Risk Prediction: A Theory-Driven Deep Learning Design Approach." Management Information Systems Quarterly 47.1 (2023): 63-96. https://aisel.aisnet.org/misq/vol47/iss1/5

Online Reviews⚓︎

  • Yu, Yifan, et al. "Unifying Algorithmic and Theoretical Perspectives: Emotions in Online Reviews and Sales."Management Information Systems Quarterly 47.1 (2023): 127-160. https://aisel.aisnet.org/misq/vol47/iss1/7
  • Yang, Mingwen, et al. "Responding to Online Reviews in Competitive Markets: A Controlled Diffusion Approach." Management Information Systems Quarterly 47.1 (2023): 161-194. https://aisel.aisnet.org/misq/vol47/iss1/8

Product Design⚓︎

  • Alex Burnap, John R. Hauser, Artem Timoshenko (2023) Product Aesthetic Design: A Machine Learning Augmentation. Marketing Science 0(0). https://doi.org/10.1287/mksc.2022.1429

© License⚓︎

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Last Update On 2023-04-15.

Copyright © 2019 - 2023 Yihong Liu
\ No newline at end of file diff --git a/search/search_index.json b/search/search_index.json index 102cb26..2d71ae6 100644 --- a/search/search_index.json +++ b/search/search_index.json @@ -1 +1 @@ -{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"intro/","title":"Introduction","text":"

What to read as a Ph.D. student or researcher majoring in Management Information Systems (MIS)?

This reading list covers MIS-relevant journals, conferences, books, papers, and resources.

A considerable number of papers in the list comes from the MIS7420 Seminar in Management Information Systems.

Ordered alphabetically, Prof. Amit Mehra, Atanu Lahiri, Jianqing Chen, Srinivasan Raghunathan, Sumit Sarkar, Syam Menon, Vijay Mookerjee, Zhiqiang Zheng are in charge of this course and contribute a lot to this list.

Thanks to them for creating a diverse topic portfolio in this MIS reading list!

"},{"location":"intro/#what-is-mis","title":"What is MIS?","text":"
  • Monideepa Tarafdar, Guohou Shan, Jason Bennett Thatcher, Alok Gupta (2022) Intellectual Diversity in IS Research: Discipline-Based Conceptualization and an Illustration from Information Systems Research. Information Systems Research 33(4):1490-1510. source
IS Methods and Theories

Information systems researchers use a bevvy of research methods and theoretical lenses to explore phenomena of interest. The following links will take you to sites that have been developed by members of the IS community who are experts in particular areas.

Theories in IS Research

Design Research

Qualitative Research

Quantitative Research

Spatial Design Support Systems

Research Task Repository

Decision Support Systems

---- AIS - IS Research, Methods, and Theories

"},{"location":"intro/#about-the-author","title":"About The Author","text":"

Yihong Liu is a Ph.D. candidate in the Management Science, Information Systems Concentration at the Naveen Jindal School of Management, UT Dallas.

"},{"location":"intro/#disclaimer","title":"Disclaimer","text":"Click to expand and read the disclaimer.

Last updated: March 08, 2022

"},{"location":"intro/#interpretation-and-definitions","title":"Interpretation and Definitions","text":""},{"location":"intro/#interpretation","title":"Interpretation","text":"

The words of which the initial letter is capitalized have meanings defined under the following conditions.

The following definitions shall have the same meaning regardless of whether they appear in singular or in plural.

"},{"location":"intro/#definitions","title":"Definitions","text":"

For the purposes of this Disclaimer:

  • We (referred to as either \"We\", \"Us\" or \"Our\" in this Disclaimer) refers to the authors of \"MIS Reading List\".

  • Service refers to the Website.

  • You means the individual accessing the Service, or the company, or other legal entity on behalf of which such individual is accessing or using the Service, as applicable.

  • Website refers to MIS Reading List, accessible from https://liu-yihong.github.io/MISReadingList/

"},{"location":"intro/#disclaimer_1","title":"Disclaimer","text":"

The information contained on the Service is for general information purposes only.

We assume no responsibility for errors or omissions in the contents of the Service.

In no event shall we be liable for any special, direct, indirect, consequential, or incidental damages or any damages whatsoever, whether in an action of contract, negligence or other tort, arising out of or in connection with the use of the Service or the contents of the Service. We reserve the right to make additions, deletions, or modifications to the contents on the Service at any time without prior notice.

We do not warrant that the Service is free of viruses or other harmful components.

"},{"location":"intro/#external-links-disclaimer","title":"External Links Disclaimer","text":"

The Service may contain links to external websites that are not provided or maintained by or in any way affiliated with us.

Please note that we do not guarantee the accuracy, relevance, timeliness, or completeness of any information on these external websites.

"},{"location":"intro/#errors-and-omissions-disclaimer","title":"Errors and Omissions Disclaimer","text":"

The information given by the Service is for general guidance on matters of interest only. Even if we take every precaution to insure that the content of the Service is both current and accurate, errors can occur. Plus, given the changing nature of laws, rules and regulations, there may be delays, omissions or inaccuracies in the information contained on the Service.

We are not responsible for any errors or omissions, or for the results obtained from the use of this information.

"},{"location":"intro/#fair-use-disclaimer","title":"Fair Use Disclaimer","text":"

We may use copyrighted material which has not always been specifically authorized by the copyright owner. We are making such material available for criticism, comment, news reporting, teaching, scholarship, or research.

We believe this constitutes a \"fair use\" of any such copyrighted material as provided for in section 107 of the United States Copyright law.

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"},{"location":"intro/#views-expressed-disclaimer","title":"Views Expressed Disclaimer","text":"

The Service may contain views and opinions which are those of the authors and do not necessarily reflect the official policy or position of any other author, agency, organization, employer or company, including us.

Comments published by users are their sole responsibility and the users will take full responsibility, liability and blame for any libel or litigation that results from something written in or as a direct result of something written in a comment. We are not liable for any comment published by users and reserves the right to delete any comment for any reason whatsoever.

"},{"location":"intro/#no-responsibility-disclaimer","title":"No Responsibility Disclaimer","text":"

The information on the Service is provided with the understanding that we are not herein engaged in rendering legal, accounting, tax, or other professional advice and services. As such, it should not be used as a substitute for consultation with professional accounting, tax, legal or other competent advisers.

In no event shall we be liable for any special, incidental, indirect, or consequential damages whatsoever arising out of or in connection with your access or use or inability to access or use the Service.

"},{"location":"intro/#use-at-your-own-risk-disclaimer","title":"\"Use at Your Own Risk\" Disclaimer","text":"

All information in the Service is provided \"as is\", with no guarantee of completeness, accuracy, timeliness or of the results obtained from the use of this information, and without warranty of any kind, express or implied, including, but not limited to warranties of performance, merchantability and fitness for a particular purpose.

We will not be liable to You or anyone else for any decision made or action taken in reliance on the information given by the Service or for any consequential, special or similar damages, even if advised of the possibility of such damages.

"},{"location":"intro/#contact-us","title":"Contact Us","text":"

If you have any questions about this Disclaimer, You can contact Us:

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"},{"location":"intro/#license","title":"License","text":"

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The emoji in the home page is designed by OpenMoji \u2013 the open-source emoji and icon project (License: CC BY-SA 4.0) and the favicon is designed by IconPark (License: Apache License 2.0).

"},{"location":"jobs/","title":"Jobs","text":"

During the months of July and August, schools will usually post openings for IS positions (note that the exact time frame may vary depending on the field, such as Marketing).

To be prepared for the hiring process, it is important to have all necessary documents ready, including your CV, teaching and research statement, teaching evaluations, recommendation letters, and any required paper copies (requirement lists may vary between schools). Keep in mind to only include projects and papers that you are well-versed in and able to clearly explain in a 2-minute summary in your CV.

You may also want to share your document package with your PhD colleagues.

"},{"location":"jobs/#is-academic-jobs-2023-2024","title":"IS Academic Jobs 2023-2024","text":"

You find new job position and want to share?

Feel free to submit the job information through this link!

Please note that jobs starting at August 2023 are excluded from this list.

"},{"location":"jobs/#where-to-find-is-jobs","title":"Where to Find IS Jobs","text":"

IS job openings can often be found at:

  1. Association for Information Systems (AIS) Career Services
  2. INFORMS Career Center
  3. Production and Operations Management Society (POMS) Placement List
  4. Interdisciplinoxy.com
  5. Akadeus.com
  6. FacultyVacancies.com
  7. AcademicJobsOnline
  8. IHE Careers
  9. AOM Career Services
  10. IS Jobs
"},{"location":"jobs/#how-is-jobs-pay","title":"How IS Jobs Pay","text":"

OpenPayrolls provides nationwide public salary database for federal agencies, states, counties, cities, universities, colleges, and K-12 schools.

"},{"location":"jobs/#states-limiting-tenure","title":"States Limiting Tenure","text":"
  1. highereddive, \"5 state (Texas, North Dakota, Louisiana, Florida, Iowa) plans to restrict faculty tenure you\u2019ll want to watch\"
  2. bestcolleges, \"Tenure Under Attack Nationwide(South Carolina, Georgia, Iowa)\"
  3. universityworldnews, \"Tenure is under attack in the US and your country is next\"
  4. thehill, \"Texas and Florida take steps to limit professor tenure at state schools\"
  5. ncnewsline, \"New bill targets tenure, calls for scrutiny of research at UNC System campuses, community colleges\"
  6. University of Tennessee, \"Periodic Post-Tenure Performance Reviews | Office of the Provost\"

Last Update On 2023-06-17.

"},{"location":"journals/","title":"MIS Journals","text":"Information Systems Research (ISR)

Information Systems Research (ISR) is an author-friendly peer-reviewed journal that publishes the best research in the information systems discipline. Its mission is to advance knowledge about the effective and efficient utilization of information technology by individuals, groups, organizations, society, and nations for the improvement of economic and social welfare.

The journal covers a wide variety of phenomena and topics related to the design, management, use, valuation, and impact of information technologies at different levels of analysis. ISR publishes research that examines topics from a wide range of research traditions including cognitive psychology, economics, computer science, operations research, design science, organization theory, organization behavior, sociology, and strategic management.

---- INFORMS - Information Systems Research / RSS Feed

Management Science (MS)

Management Science (MS) is a scholarly journal that publishes scientific research on the practice of management focusing on the problems, interest, and concerns of managers.

Within its scope are all aspects of management related to strategy, entrepreneurship, innovation, information technology, and organizations as well as all functional areas of business, such as accounting, finance, marketing, and operations.

---- INFORMS - Management Science / RSS Feed

MIS Quarterly (MISQ)

The MIS Quarterly\u2019s trifecta vision is to

(1) achieve impact on scholarship and practice as the leading source of novel and accreted IS knowledge,

(2) exhibit range in work published with respect to problem domains and stakeholders addressed as well as theoretical and methodological approaches used, and

(3) execute effective editorial processes in a timely manner.

---- MIS Quarterly / Unofficial RSS Feed

AIS - Senior Scholars' List of Premier Journals

The College of Senior Scholars encourages colleagues, as well as deans and department chairs, to treat a list of premier journals as the top journals in our field. Such a list is intended to provide more consistency and meaningfulness to tenure and promotion cases.

The journal list is limited to those in the \"IS field,\" and omits both multidisciplinary outlets and specialty areas. Nevertheless, the list recognizes topical, methodological, and geographical diversity. In addition, the review processes are stringent, editorial board members are widely-respected and recognized, and there is international readership and contribution.

The journals in the list are, in alphabetical order:

Decision Support Systems

European Journal of Information Systems

Information & Management

Information and Organization

Information Systems Journal

Information Systems Research

Journal of the AIS

Journal of Information Technology

Journal of MIS

Journal of Strategic Information Systems

MIS Quarterly

---- Senior Scholars' List of Premier Journals / Rankings of universities and authors based on the Senior Scholars' Basket of Journals

UTD24

The UT Dallas\u2019 Naveen Jindal School of Management has created a database to track publications in 24 leading business journals.

The database contains titles and author affiliations of papers published in these journals since 1990.

The information in the database is used to provide the top 100 business school rankings since 1990 based on the total contributions of faculty.

---- UTD24

FT50

The Financial Times conducted a review in May 2016 of the journals that count towards its research rank. As a result, the number of journals considered went up to 50 compared to 45 previously.

---- FT50 / archive

"},{"location":"journals/#other-journals","title":"Other Journals","text":""},{"location":"journals/#license","title":"License","text":"

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Last Update On 2023-03-03.

"},{"location":"references/","title":"Reference Books & Papers","text":""},{"location":"references/#the-academic-life","title":"The Academic Life","text":""},{"location":"references/#advice-tips","title":"Advice & Tips","text":"
  • Richard Hamming, \"You and Your Research\", 1986. source archive
  • Peters, Robert L. Getting what you came for: the smart student's guide to earning a master's or a Ph. D. New York: Farrar, Straus and Giroux, 1997. source
  • Booth, W. C., Booth, W. C., Colomb, G. G., Colomb, G. G., Williams, J. M., & Williams, J. M. (2003). \"The craft of research\". University of Chicago press. source
  • Fei-Fei Li, \"De-Mystifying Good Research and Good Papers\", 2009. source archive
  • Feibelman, Peter J. \"A PhD is not enough!: A guide to survival in science\". Basic Books, 2011.
  • Phillips, Estelle, and Derek Pugh. \"How to Get a PhD: A Handbook for Students and their Supervisors\". McGraw-Hill Education (UK), 2015.
  • Andrej Karpathy, \"A Survival Guide to a PhD\", 2016. source archive
  • Volkan Cirik, \"PhD 101\", 2019. source archive
  • Sam Altman, \"How To Be Successful\", 2019. source archive
  • Sebastian Ruder, \"10 Tips for Research and a PhD\", 2020. source archive
  • Isabelle Augenstein, \"Increasing Well-Being in Academia\", 2020. source archive
  • Li, Longxing. \"The A-Z of the PhD Trajectory: A Practical Guide for a Successful Journey.\" International Journal of Teaching and Learning in Higher Education 32.3 (2020): 536-538. source
  • Banafsheh Behzad and Xiaonan (Shannon) Shang, \"Transitioning from Student to Professional\" INFORMS Speakers Program, 2022. video
"},{"location":"references/#academic-writings","title":"Academic Writings","text":"
  • Cochrane, John H. \"Writing Tips for Ph. D. Students.\" Chicago, IL: University of Chicago, 2005. pdf Chinese Version
  • Zinsser, William. \"On writing well: The classic guide to writing nonfiction.\" New York, NY (2006). source
  • Clark, R. P. (2008). \"Writing tools: 55 essential strategies for every writer\". Little, Brown Spark. source
  • McCarthy, Michael, and Felicity O'dell. Academic vocabulary in use. Ernst Klett Sprachen, 2008.
  • Sword, H. (2012).\"Stylish academic writing\". Harvard University Press. source
  • Brittman, Felicia. \"The Most Common Habits from more than 200 English Papers written by Graduate Chinese Engineering Students.\" (2011). pdf
  • Swales, J.M. et al. \"Academic Writing for Graduate Students: Essential Tasks and Skills\". University of Michigan Press, 2012. source
  • Clark, Roy Peter. How to write short: Word craft for fast times. Little, Brown Spark, 2013. source
  • Bailey, Stephen. Academic writing: A handbook for international students. Routledge, 2014. source
  • Morley, John. \"Academic phrasebank.\" Manchester: University of Manchester (2014). source
  • Wallwork, A. (2016). \"English for writing research papers\". Springer. source
  • Card, Stuart K. \"The PhD Thesis Deconstructed.\" IEEE Computer Graphics and Applications 36.04 (2016): 92-101. source
  • Silvia, Paul J. How to write a lot: A practical guide to productive academic writing. American Psychological Association, 2018. source
  • INFOGRAPHIC: The secret to using tenses in scientific writing source
  • Using tenses in scientific writing: Tense considerations for science writing pdf
  • Struijk, Myl\u00e8ne, et al. \"Putting the IS back into IS research.\" Information Systems Journal (2021). source
"},{"location":"references/#review-writings","title":"Review Writings","text":"
  • Elisabeth PainSep. 22, 2. (2016, September 22). \"How to review a paper\". Retrieved August 31, 2020. source archive
  • Rai, A. (2016). Editor's comments: writing a virtuous review. MIS Quarterly, 40(3), iii-x. pdf
  • Wiley. \"How to perform a peer review\". Retrieved August 31, 2020. source
  • Indiana University East. (2017). \"How to Write a Review of a Scholarly Article\". Retrieved August 31, 2020. source
  • Adams, J. (2020, August 6). \"How to Write an Article Review\". source
  • Ahmed, B. S. (2018, February 26). \"Tips and advice when you review a scientific paper\". Elsevier. source
  • MIS Quarterly. (2020). \"Reviewing for MIS Quarterly: Virtuous Reviewing at MIS Quarterly\". source.
"},{"location":"references/#response-to-reviews","title":"Response to Reviews","text":"
  • Pang, Min-Seok and Thatcher, Jason B. (2023) \"A Practical Guide for Successful Revisions and Engagements with Reviewers,\" Journal of the Association for Information Systems, 24(2), 317-327. source
"},{"location":"references/#academic-presentations","title":"Academic Presentations","text":"
  • Reinhart, Susan M. Giving academic presentations. Ann Arbor, MI: University of Michigan Press, 2002. source
  • Chivers, Barbara, and Michael Shoolbred. A Student\u2032 s Guide to Presentations: Making your Presentation Count. Sage, 2007. source
  • Graham Burton. Presenting: Deliver Academic Presentations with Confidence HarperCollins UK, 2014.
  • Rendle-Short, Johanna. The academic presentation: Situated talk in action. Routledge, 2016.
  • Nycyk, Michael. \"Academic and scientific poster presentation: a modern comprehensive guide.\" (2018): 1550-1552.
  • Guest, Michael. Conferencing and Presentation English for Young Academic. Springer, 2018. source
"},{"location":"references/#academic-career","title":"Academic Career","text":"
  • Showalter, English. The MLA guide to the job search: A handbook for departments and for PhDs and PhD candidates in English and foreign languages. Modern Language Assoc. of America, 1996.
  • Goldsmith, John A., John Komlos, and Penny Schine Gold. The Chicago guide to your academic career: A portable mentor for scholars from graduate school through tenure. University of Chicago Press, 2001.
  • Kelsky, Karen. The professor is in: The essential guide to turning your Ph. D. into a job. Crown, 2015. homepage
  • Vick, Julia Miller, Jennifer S. Furlong, and Rosanne Lurie. \"The academic job search handbook.\" The Academic Job Search Handbook. University of Pennsylvania Press, 2016.
  • Boice, Robert. Advice for new faculty members. Vol. 75. Needham Heights, MA: Allyn & Bacon, 2000.
  • Firth, David, Matt Germonprez, and Jason Thatcher. \"Managing your PhD student career: How to prepare for the job market.\" Communications of the Association for Information Systems 34.1 (2014): 5. source
  • Liu, Yihong. \"Notes for Ph.D. Job Interview Experiences 02\u201321-2020.\" Yihong Liu\u2019s Blog, 26 May 2020, source.
  • Baquero, Carlos. \"Publishing, The Choice and The Luck.\" blog@CACM | Communications of the ACM, Communications of the ACM, 22 Nov. 2021, source archive.
  • Baquero, Carlos. \"Picking Publication Targets.\" March 2022 | Communications of the ACM, Communications of the ACM, 1 Mar. 2022, source archive.
  • Association for Information Systems (AIS) Career Services
  • INFORMS Career Center
  • Production and Operations Management Society (POMS) Placement List
  • Interdisciplinoxy.com
  • Akadeus.com
"},{"location":"references/#teaching","title":"Teaching","text":"
  • Bain, Ken. What the best college teachers do. Harvard University Press, 2004. source
  • Filene, Peter. The joy of teaching: A practical guide for new college instructors. Univ of North Carolina Press, 2009. source
  • Seldin, Peter, J. Elizabeth Miller, and Clement A. Seldin. The teaching portfolio: A practical guide to improved performance and promotion/tenure decisions. John Wiley & Sons, 2010. source
  • Colby, Anne, et al. Rethinking undergraduate business education: Liberal learning for the profession. John Wiley & Sons, 2011. source
  • Bowen, Jos\u00e9 Antonio. Teaching naked: How moving technology out of your college classroom will improve student learning. John Wiley & Sons, 2012.
  • Angelo, Thomas A., and K. Patricia Cross. Classroom Assessment Techniques: A Handbook for College Teachers. Jossey Bass Wiley, 2012. source
  • Lang, James M. Cheating Lessons: Learning from Academic Dishonesty. Harvard University Press, 2013. source
  • Doyle, Elaine, Patrick Buckley, and Conor Carroll, eds. Innovative business school teaching: Engaging the millennial generation. Routledge, 2014. source
  • David Gooblar, \"They Haven\u2019t Done the Reading. Again.\", 2014. archive
  • Carey, Benedict. How we learn: The surprising truth about when, where, and why it happens. Random House Trade Paperbacks, 2015.
  • Nilson, Linda B. Specifications grading: Restoring rigor, motivating students, and saving faculty time. Stylus Publishing, LLC, 2015. source
  • Henderson, Linda J. \"Start Talking: A Handbook for Engaging Difficult Dialogues in Higher Education.\" (2016): 56-60. source
  • Nilson, Linda B. Teaching at its best: A research-based resource for college instructors. John Wiley & Sons, 2016. source
  • Howard, Jay. \"Class Discussion: From Blank Stares to True Engagement.\", 2019. source archive
  • Gilmore, Joanna, and Molly Hatcher, eds. Preparing for College and University Teaching: Competencies for Graduate and Professional Students. Stylus Publishing, LLC, 2021. source
  • M\u00fcller, S. D. (2022). Student Research as Legitimate Peripheral Participation. Communications of the Association for Information Systems, 50, pp-pp. source
  • Zheng, Lily. DEI Deconstructed: Your No-nonsense Guide to Doing the Work and Doing it Right. Berrett-Koehler Publishers, 2022. source
  • Daniel T. Willingham. Outsmart Your Brain: Why Learning is Hard and How You Can Make It Easy. Gallery Books, 2023. source
  • Regan A. R. Gurung and John Dunlosky. Study Like a Champ: The Psychology-Based Guide to \u201cGrade A\u201d Study Habits. APA LifeTools, 2023. source
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  • Journal of Management Education
  • Management Teaching Review
  • Journal of Teaching in International Business
  • Journal of Education for Business
"},{"location":"references/#ai-education","title":"AI & Education","text":"

This list comes from the Center of Teaching and Learning at the University of Texas at Dallas along with other sources.

  • Julia Staffel, ChatGPT and Its Impact on Teaching Philosophy and Other Subjects video
  • Cynthia Alby, Chatgpt: Understanding the New Landscape and Short-Term Solutions Google Docs
  • Lee Skallerup Bessette's Zotero Library on ChatGPT source
  • Teachers On Fire, Should Schools BAN ChatGPT? 4 Reasons Not To! video
  • Eric Prochaska, Embrace the Bot: Designing Writing Assignments in the Face of AI source
  • Alexandra Mihai, Let's get off the fear carousel! source
  • Art Brownlow, AI Essay Writing: Dawn in the Garden of Good and Evil video
  • Kritik Education, 12 Ways Instructors Can Use OpenAI to Transform Assessments source
  • Derek Bruff, A Bigger, Badder Clippy: Enhancing Student Learning with AI Writing Tools source
  • @herfteducator, A Teacher\u2019s Prompt Guide to ChatGPT Aligned With 'What Works Best' pdf
  • Center for Teaching & Assessment of Learning @ University of Delaware, Considerations for Using and Addressing Advanced Automated Tools in Coursework and Assignments website
  • Gabby Jones / Bloomberg, ChatGPT Is a Wake-up Call to Revamp How We Teach Writing website
  • Joshua Wilson, Writing Without Thinking? There\u2019s a Place for ChatGPT \u2014 If Used Properly website
  • turnitin.com, AI-generated text: What educators are\u00a0saying source
  • turnitin.com, AI-generated text: An annotated hotlist for\u00a0educators source
  • turnitin.com, Guide for approaching AI-generated text in your\u00a0classroom source
  • SAN JOS\u00c9 STATE UNIVERSITY, Generative AI & ChatGPT: Resources for Instructors source
  • UCLA, ChatGPT and AI Resources source
  • Lance Eaton, Classroom Policies for AI Generative Tools source
"},{"location":"references/#ethics","title":"Ethics","text":"
  • Davison, Robert M., Maris G. Martinsons, and Louie HM Wong. \"The ethics of action research participation.\" Information Systems Journal (2021). source
  • Umphress, Elizabeth E., et al. \"From the Editors: Insights on how we try to show empathy, respect, and inclusion in AMJ.\" Academy of Management Journal (2022). source
"},{"location":"references/#dress-code","title":"Dress Code","text":"
  • Lee, Christopher. \"Dressing the Professor: What to Wear for Working in Academia.\" Gentleman's Gazette, 8 Nov. 2018. source archive
  • Block, Marta Segal. \"What to Wear on Campus.\" HigherEdJobs, 18 Apr. 2017. source archive
  • martinkich. \"Student and Faculty Dress Codes.\" ACADEME BLOG, 5 Feb. 2015. source archive
  • Smart, Michael. \"How a Professor Should Dress: Tips for Lecturers, Tas & Teachers.\" LearnPar, 21 May 202. source archive
  • Lightstone, Karen, Rob Francis, and Lucie Kocum. \"University faculty style of dress and students' perception of instructor credibility.\" International Journal of Business and Social Science 2.15 (2011). source
  • 40+Style. \"What to Wear to a Conference or Presentation to Be Stylish and Professional.\" 40+ Style, 3 Aug. 2020. source
  • Crestline. \"What to Wear to a Conference: The Ultimate Guide.\" Crestline, 28 Feb. 2022. source
  • Monus, Elle. \"3 Ways to Dress for a Conference.\" WikiHow, WikiHow, 10 Oct. 2021. source
"},{"location":"references/#statistics-and-probability","title":"Statistics and Probability","text":"
  • Casella, George, and Roger L. Berger. Statistical inference. Vol. 2. Pacific Grove, CA: Duxbury, 2002. source
"},{"location":"references/#optimization","title":"Optimization","text":"
  • Boyd, Stephen, Stephen P. Boyd, and Lieven Vandenberghe. Convex optimization. Cambridge university press, 2004. pdf
  • Sundaram, Rangarajan K. A first course in optimization theory. Cambridge university press, 1996.
"},{"location":"references/#bayesian-optimization","title":"Bayesian Optimization","text":"
  • Brochu, Eric, Vlad M. Cora, and Nando De Freitas. \"A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning.\" arXiv preprint arXiv:1012.2599 (2010). source
  • Shahriari, Bobak, et al. \"Taking the human out of the loop: A review of Bayesian optimization.\" Proceedings of the IEEE 104.1 (2015): 148-175. source
  • Frazier, Peter I. \"Bayesian optimization.\" Recent advances in optimization and modeling of contemporary problems. Informs, 2018. 255-278. source
"},{"location":"references/#microeconomic","title":"Microeconomic","text":"
  • Varian, Hal R. Microeconomic analysis. WW Norton, 1992.
  • Rubinstein, Ariel. Lecture notes in microeconomic theory: the economic agent. Princeton University Press, 2012. pdf
"},{"location":"references/#econometrics","title":"Econometrics","text":"
  • Greene, William H. Econometric analysis (Eight Edition). (2017).
  • Cameron, A. Colin, and Pravin K. Trivedi. Microeconometrics: methods and applications. Cambridge university press, 2005.
  • Wooldridge, Jeffrey M. Econometric analysis of cross section and panel data. MIT press, 2010.
  • Wooldridge, Jeffrey M. Introductory econometrics: A modern approach. Nelson Education, 2016.
  • Hill, R. Carter, William E. Griffiths, and Guay C. Lim. Principles of econometrics. John Wiley & Sons, 2018.
  • Davidson, Russell, and James G. MacKinnon. Econometric theory and methods. Vol. 5. New York: Oxford University Press, 2004.
  • Maddala, Gangadharrao S. Limited-dependent and qualitative variables in econometrics. No. 3. Cambridge university press, 1986.
  • Angrist, Joshua D., and J\u00f6rn-Steffen Pischke. Mostly harmless econometrics: An empiricist's companion. Princeton university press, 2008.
  • Baltagi, Badi Hani. \"Econometric analysis of panel data\". Springer International Publishing, (6th Edition, 2021). source
  • This Wikipedia page compares technical information for a number of statistical analysis packages.
"},{"location":"references/#causal-inference","title":"Causal Inference","text":"
  • Pearl, Judea. \"Causal inference in statistics: An overview.\" Statistics surveys 3 (2009): 96-146. pdf
  • Pearl, Judea. Causality. Cambridge university press, 2009. author's website
  • Hern\u00e1n, Miguel A., and James M. Robins. \"Causal inference.\" (2010): 2. source
  • Glymour, Madelyn, Judea Pearl, and Nicholas P. Jewell. Causal inference in statistics: A primer. John Wiley & Sons, 2016. author's website
  • Peters, Jonas, Dominik Janzing, and Bernhard Sch\u00f6lkopf. Elements of causal inference: foundations and learning algorithms. The MIT Press, 2017. source
  • Pearl, Judea, and Dana Mackenzie. The book of why: the new science of cause and effect. Basic books, 2018. author's website
  • Yao, Liuyi, et al. \"A survey on causal inference.\" arXiv preprint arXiv:2002.02770 (2020). source
  • Cunningham, Scott. \"Causal inference.\" Causal Inference. Yale University Press, 2021. author's website
  • Matheus Facure's handbook Causal Inference for The Brave and True github repository
  • Brady Neal's blog provides a good list of books
  • Brady Neal's blog also provides a good course \"Introduction to Causal Inference\"
"},{"location":"references/#game-theory","title":"Game Theory","text":"
  • Gibbons, Robert S. Game theory for applied economists. Princeton University Press, 1992.
  • Fudenberg, Drew, and Jean Tirole. Game theory. MIT press, 1991.
  • Myerson, Roger B. Game Theory: Analysis of Conflict. Harvard university press, 2013.
  • Osborne, Martin J., and Ariel Rubinstein. A course in game theory. MIT press, 1994.
"},{"location":"references/#industry-organization","title":"Industry Organization","text":"
  • Tirole, Jean. The theory of industrial organization. MIT press, 1988.
  • Vives, Xavier. Oligopoly pricing: old ideas and new tools. MIT press, 1999.
  • Martin, Stephen. Advanced industrial economics. Blackwell Publishers, 2002.
  • Belleflamme, Paul, and Martin Peitz. Industrial organization: markets and strategies. Cambridge University Press, 2015.
"},{"location":"references/#artificial-intelligence","title":"Artificial Intelligence","text":"
  • Shmueli, Galit. \"To explain or to predict?.\" Statistical science 25.3 (2010): 289-310. source
  • Shmueli, Galit, and Otto R. Koppius. \"Predictive analytics in information systems research.\" MIS quarterly (2011): 553-572. source
"},{"location":"references/#machine-learning","title":"Machine Learning","text":"
  • Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media, 2009. source
  • Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006. pdf
  • Barber, David. Bayesian reasoning and machine learning. Cambridge University Press, 2012. source
  • Murphy, Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.
  • Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. \"Deep learning.\" Cambridge: MIT press, 2016. source
  • Sergey Levine provides a course CS W182 / 282A - UC Berkeley at Designing, Visualizing and Understanding Deep Neural Networks
  • Murphy, Kevin P. Probabilistic machine learning: an introduction. MIT press, 2022. website
  • Murphy, Kevin P. Probabilistic machine learning: Advanced Topics. MIT press, 2023. website
  • Berente, Nicholas, et al. \"Managing artificial intelligence.\" MIS Quarterly 45.3 (2021): 1433-1450. source
  • Balaji Padmanabhan, Xiao Fang, Nachiketa Sahoo, and Andrew Burton-Jones. \"Machine Learning in Information Systems Research\", MIS Quarterly Editors' Comments, 2022. source
"},{"location":"references/#few-shot-learning","title":"Few-shot Learning","text":"
  • Wang, Yaqing, et al. \"Generalizing from a few examples: A survey on few-shot learning.\" ACM Computing Surveys (CSUR) 53.3 (2020): 1-34. source
"},{"location":"references/#transfer-learning","title":"Transfer Learning","text":"
  • Pan, Sinno Jialin, and Qiang Yang. \"A survey on transfer learning.\" IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359. source
  • Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. \"A survey of transfer learning.\" Journal of Big data 3.1 (2016): 1-40. source
  • Tan, Chuanqi, et al. \"A survey on deep transfer learning.\" International conference on artificial neural networks. Springer, Cham, 2018. source
  • Zhuang, Fuzhen, et al. \"A comprehensive survey on transfer learning.\" Proceedings of the IEEE 109.1 (2020): 43-76. source
"},{"location":"references/#reinforcement-learning","title":"Reinforcement Learning","text":"
  • Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. MIT press, 2018. pdf
  • Yang, Tianpei, et al. \"Exploration in deep reinforcement learning: A comprehensive survey.\" arXiv preprint arXiv:2109.06668 (2021). source
  • datawhalechina on github.com provides a course in Chinese at datawhalechina/easy-rl
  • Bolei Zhou on github.com provides a course in English at zhoubolei/introRL
  • DeepMind and UCL provide an introduction lecture on reinforcement learning at Reinforcement Learning Lecture Series 2021
  • Hao Dong, Zihan Ding and Shanghang Zhang offer an online version of the book \"Deep Reinforcement Learning: Fundamentals, Research and Applications\"
"},{"location":"references/#computer-vision","title":"Computer Vision","text":"
  • jbhuang0604 on github.com provides a curated list of computer vision resources at awesome-computer-vision
"},{"location":"references/#natural-language-processing","title":"Natural Language Processing","text":"
  • keon on github.com provides a curated list dedicated to natural language processing at awesome-nlp
"},{"location":"references/#stochastic-differential-equation","title":"Stochastic Differential Equation","text":"
  • Gardiner, Crispin W. \"Handbook of stochastic methods: for physics, chemistry and the natural sciences.\" (2004). source
  • Dixit, Robert K., and Robert S. Pindyck. \"Investment under uncertainty\". Princeton university press, 2012. source
  • Klebaner, Fima C. \"Introduction to stochastic calculus with applications\". World Scientific Publishing Company, 2012. source
  • Evans, Lawrence C. \"An introduction to stochastic differential equations\". Vol. 82. American Mathematical Soc., 2012. pdf
  • Mikosch, Thomas. \"Elementary stochastic calculus with finance in view\". World scientific, 1998. source
  • Oksendal, Bernt. \"Stochastic differential equations: an introduction with applications\". Springer Science & Business Media, 2013. source
  • Mao, Xuerong. \"Stochastic differential equations and applications\". Elsevier, 2007. source
"},{"location":"references/#theory-building","title":"Theory Building","text":"
  • Hassan, Nik Rushdi; Lowry, Paul Benjamin; and Mathiassen, Lars (2022) \"Useful Products in Information Systems Theorizing: A Discursive Formation Perspective,\" Journal of the Association for Information Systems, 23(2), 418-446. source
"},{"location":"references/#code-analysis","title":"Code Analysis","text":"
  • AnalysisTools
  • SonarQube
  • Codacy
  • deepsource
  • Semgrep
  • embold
  • Coverity Scan
"},{"location":"references/#license","title":"License","text":"

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Last Update On 2023-06-02.

"},{"location":"conferences/","title":"MIS Conferences","text":"The International Conference on Information Systems (ICIS)

The International Conference on Information Systems (ICIS) is the most prestigious gathering of information systems academics and research-oriented practitioners in the world. Every year its 270 or so papers and panel presentations are selected from more than 800 submissions. The conference activities are primarily delivered by and for academics, though many of the papers and panels have a strong professional orientation.

ICIS was founded in 1980 at UCLA and the first conference was held at the University of Pennsylvania as the \" Conference on Information Systems\". By 1986, particularly as the result of Canadian and European attendance and participation, \" International\" was appended to the name, thereby creating the International Conference on Information Systems. ICIS became truly international in 1990 when the conference was first held outside North America in Copenhagen, Denmark.

---- ICIS Home Page / ICIS Proceedings / 2023

Conference on Information Systems and Technology (CIST): 2022, 2023

Workshop on Information Systems and Economics (WISE)

Workshop on Information Technologies and Systems (WITS)

WITS, the Workshop on Information Technologies and Systems is an academic conference for information systems that is held annually in December in conjunction with ICIS (the International Conference on Information Systems).

The WITS community is focused on addressing complex business problems or societal issues using current and emerging information technologies.\u00a0 We also encourage research that can change the way information technology functions (e.g., by designing, modifying, or constructing systems) so that they can better solve real-world problems. All problem-solving paradigms \u2013 including empirical, analytical, behavioral, experimental, and computational \u2013 are invited. Integrative approaches, whether methodological or functional, are welcome.

WITS research is often\u00a0prescriptive\u00a0(toward providing a solution to a problem), rather than\u00a0descriptive\u00a0(explaining a phenomenon), unless the explanation clearly helps in developing a solution. We particularly invite work that is early, but has the potential to make a significant impact \u2013 innovation and novelty are at least as important as completeness and rigor.

---- WITS Home Page

Theory in Economics of Information Systems (TEIS)

The TEIS workshop is designed to provide a community for researchers who use analytical modeling techniques in the area of economics of information systems. Although a number of workshops and conferences accept research based on analytical models, these tend to be diffused with inadequate time for presentation, discussion and Q&A.

TEIS workshop complements such venues by providing a focused and intense environment for interaction among researchers to assist in the development of the field and help advance shared understanding about various aspects of modeling research. TEIS workshops have a single track with one hour per paper so everyone can participate substantively in the discussion.

---- TEIS Home Page

Statistical Challenges in Electronic Commerce Research (SCECR)

Started in 2005 by Ravi Bapna (currently at University of Minnesota), Wolfgang Jank (currently at University of South Florida), and Galit Schmueli (National Tsing Hua University), the Workshop on Statistical Conference in E-Commerce Research (SCECR) is a leading workshop attracting many top researchers throughout the world in the areas of information systems, quantitative marketing, economics,\u00a0 statistics, machine learning, and computer science.

The workshop covers diverse domains such as e-commerce, social media, digital content, finance, and telecommunications.\u00a0 Methods include econometric, machine learning, statistical inference, and unstructured and Big Data techniques.\u00a0 The theme this year will be related to Big Data and economic impact.

---- SCECR Home Page

Conference on Health IT and Analytics (CHITA)

The Conference on Health IT and Analytics (previously known as the Workshop on Health IT & Economics) is an annual health IT and analytics research summit, including a doctoral consortium that each year gathers prominent scholars from more than 40 research institutes, and leading policy and practitioner attendees in a vibrant setting to discuss opportunities and challenges in the design, implementation and management of health information technology and analytics.

Its goal is to deepen our understanding of strategy, policy and systems fostering health IT and analytics effective use and to stimulate new ideas with both policy and business implications. This forum provides a productive venue to facilitate collaboration among academia, government, and industry. Now in its eleventh year, each year CHITA draws over 120 participants.

---- 2022 / 2023

Hawaii International Conference on System Sciences (HICSS)

Since 1968, the Hawaii International Conference on System Sciences (HICSS) has been known worldwide as the longest-standing working scientific conferences in Information Technology Management.

HICSS provides a highly interactive working environment for top scholars from academia and the industry from over 60 countries to exchange ideas in various areas of information, computer, and system sciences. According to Microsoft Academic, HICSS ranks the 36th in terms of citations among 4,444 conferences in all fields worldwide.

---- HICSS Home Page

China Summer Workshop on Information Management (CSWIM)

As China has become a major player in the world\u2019s economy and various technological fields, information systems and management research opportunities are abundant for scholars around the globe.

The purpose of China Summer Workshop on Information Management (CSWIM) is to create a new bridge for promoting exchanges between scholars in China and overseas in the area of information systems and management. In particular, CSWIM focuses on creating a unique experience for MIS researchers around the world who would like to communicate and collaborate with China-based scholars.

---- CSWIM Home Page

China Workshop on Economics of Information Systems Theory (CWEIST)

The field of information systems has a long tradition of using analytical modeling (e.g. game-theoretical and mathematical models) to understand information systems phenomena and generate useful recommendations. With many emerging phenomena in IS, the need for this type of applied theory research is ever greater. However, the forums for this style of inquiry are rather limited, especially for analytical modeling scholars in China and the surrounding regions. The purpose of the China Workshop on Economics of Information Systems Theory (CWEIST) is to bring together a community of scholars in China and around the world with a shared interest in using analytical modeling to study issues in IS and related fields. We hope this summer workshop to become a unique forum for this community to exchange ideas, hone our skills, and form new collaborations across geographical boundaries.

---- CWEIST Home Page / 2023

Production and Operations Management Society (POMS) Conference

Production and Operations Management Society (POMS) is an international professional organization representing the interests of POM professionals from around the world.

The purposes of the Society are:

  • to extend and integrate knowledge that contributes to the improved understanding and practice of production and operations management (POM);
  • to disseminate information on POM to managers, scientists, educators, students, public and private organizations, national and local governments, and the general public; and
  • to promote the improvement of POM and its teaching in public and private manufacturing and service organizations throughout the world

---- POMS Home Page / POMS Conference Page / 2023

INFORMS Annual Meeting

The INFORMS Annual Meeting brings together over 6,000 people to the world's largest O.R. and analytics conference. Held each fall, the INFORMS Annual Meeting features more than 800 sessions and presentations, opportunities to meet with leading companies, universities and other exhibitors, an onsite career fair connecting top talent with over 100 organizations at the forefront of O.R. and analytics application, and other networking and educational events.

---- INFORMS Conference Home Page / 2023 INFORMS Annual Meeting

The Americas Conference on Information Systems (AMCIS)

The annual Americas Conference on Information Systems (AMCIS) is viewed as one of the leading conferences for presenting the broadest variety of research done by and for IS/IT academicians. Every year its papers and panel presentations are selected from over 700 submissions, and the AMCIS proceedings are in the permanent collections of libraries throughout the world.

---- AMCIS Home Page / AMCIS Proceedings

Pacific Asia\u00a0Conference on Information Systems (PACIS)

The annual Pacific Asia Conference on Information Systems (PACIS) is viewed as one of the leading conferences on information systems and the only AIS conference dedicated to the Pacific Asia Region. PACIS is endorsed by the AIS Council and governed by the AIS Region 3 Board.

---- PACIS Home Page / PACIS Proceedings

European Conference on Information Systems (ECIS)

The annual European Conference on Information Systems (ECIS) is viewed as one of the leading conferences on information systems and the only AIS conference\u00a0dedicated to\u00a0the European Region. ECIS is the newest regional conference endorsed by the AIS Council and governed by the AIS Region 2 Board.

---- ECIS Home Page / ECIS Proceedings

International Conference on Design Science Research in Information Systems and Technology (DESRIST)

Design\u00a0science research (DSR) in information\u00a0systems (IS) has received significant\u00a0attention in the information systems\u00a0research community. In an immersed\u00a0society, where there are numerous\u00a0wicked problems on all levels of analysis,\u00a0DSR is an ideal approach to understand\u00a0\u00a0complex challenges and support the\u00a0design of useful solutions, making\u00a0provision for rigour and relevance.\u00a0Based on multi-stakeholder problem\u00a0analysis and informed by existing\u00a0descriptive and design knowledge,\u00a0well-designed innovative methods,\u00a0solution patterns, reference models and\u00a0exemplary IS solutions promise to be\u00a0effective means of addressing many of\u00a0today\u2019s challenges \u2013 and will contribute\u00a0to the further development of DSR\u2019s\u00a0methodological foundations. The\u00a0better we get at integrating humans,\u00a0organisations and machines, the better\u00a0we will be able to use all means possible\u00a0to achieve the Sustainable Development\u00a0Goals (SDGs). The United Nations, with\u00a0its economic and social development\u00a0agenda, as it pertains to sustainability, ultimately impacts all countries,\u00a0organisations, teams and individuals through the SDGs.

---- 2023 / 2022 / 2021 / Springer Conference Proceedings List

IADIS Information Systems Conference

The IADIS Information Systems Conference aims to provide a forum for the discussion of IS taking a socio-technological perspective. It aims to address the issues related to design, development and use of IS in organisations from a socio-technological perspective, as well as to discuss IS professional practice, research and teaching.

---- 2023 / IADIS

International Conference on Information Systems Security and Privacy (ICISSP)

The International Conference on Information Systems Security and Privacy provides a meeting point for researchers and practitioners, addressing the trust, security and privacy challenges of information systems from both technological and social perspectives.

The conference welcomes papers of either practical or theoretical nature, and is interested in research or applications addressing all aspects of trust, security and privacy, and encompassing issue of concern for organizations, individuals and society at large.

---- ICISSP Home Page

INFORMS Conference Calendar

Conference Index - Information Systems Conferences

"},{"location":"conferences/#license","title":"License","text":"

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"},{"location":"research/analytical/","title":"Analytical Model","text":""},{"location":"research/analytical/#how-to-model","title":"How to Model","text":"
  • Varian, Hal R. \"How to build an economic model in your spare time.\" The American Economist 61.1 (2016): 81-90. pdf
  • Tobias Cr\u00f6nert, Stefan Minner (2022) Equilibrium Identification and Selection in Finite Games. Operations Research 0(0). source
"},{"location":"research/analytical/#bounded-rationality-and-attention","title":"Bounded Rationality and Attention","text":"
  • Gifford, Sharon. \"Limited attention as the bound on rationality.\" The BE Journal of Theoretical Economics 5.1 (2005). source
"},{"location":"research/analytical/#adverse-selection-and-self-selection","title":"Adverse Selection and Self Selection","text":"
  • Akerlof, George A. \"The market for \u201clemons\u201d: Quality uncertainty and the market mechanism.\" Uncertainty in economics. Academic Press, 1978. 235-251. source
  • Sundararajan, Arun. \"Nonlinear pricing of information goods.\" Management science 50.12 (2004): 1660-1673. source
  • Samir Mamadehussene (2023) Rebates Offered by a Multiproduct Firm. Marketing Science 0(0). https://doi.org/10.1287/mksc.2023.1430
"},{"location":"research/analytical/#two-sided-market","title":"Two-sided Market","text":"
  • Tunc, Murat M., Huseyin Cavusoglu, and Srinivasan Raghunathan. \"Two-Sided Adverse Selection and Bilateral Reviews in Sharing Economy.\" Available at SSRN 3499979 (2019). source
  • Yifan Dou, D. J. Wu (2021) Platform Competition Under Network Effects: Piggybacking and Optimal Subsidization. Information Systems Research 32(3):820-835. source
  • Manlu Chen, Ming Hu, Jianfu Wang (2022) Food Delivery Service and Restaurant: Friend or Foe?. Management Science 0(0). source
  • Saeed Alaei, Ali Makhdoumi, Azarakhsh Malekian, Sa\u0161a Peke\u010d (2022) Revenue-Sharing Allocation Strategies for Two-Sided Media Platforms: Pro-Rata vs. User-Centric. Management Science 0(0). source
  • Haurand, M. D. (2022). Looking Beyond Membership: A Simulation Study of Market-entry Strategies for Two-sided Platforms under Competition. Communications of the Association for Information Systems, 50, pp-pp. source
  • Elias Carroni, Leonardo Madio, Shiva Shekhar (2023) Superstar Exclusivity in Two-Sided Markets. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4720
"},{"location":"research/analytical/#externalities","title":"Externalities","text":"
  • August, Terrence, and Tunay I. Tunca. \"Network software security and user incentives.\" Management Science 52.11 (2006): 1703-1720. source
  • Koh, Byungwan, Srinivasan Raghunathan, and Barrie R. Nault. \"Is voluntary profiling welfare enhancing?.\" Management Information Systems Quarterly, Forthcoming (2015). source
"},{"location":"research/analytical/#platform","title":"Platform","text":""},{"location":"research/analytical/#online-platform","title":"Online Platform","text":"
  • Alexandre de Corni\u00e8re, Miklos Sarvary (2022) Social Media and News: Content Bundling and News Quality. Management Science 0(0). source
  • Yunke Mai, Bin Hu, Sa\u0161a Peke\u010d (2022) Courteous or Crude? Managing User Conduct to Improve On-Demand Service Platform Performance. Management Science 0(0). source
  • Pnina Feldman, Andrew E. Frazelle, Robert Swinney (2022) Managing Relationships Between Restaurants and Food Delivery Platforms: Conflict, Contracts, and Coordination. Management Science 0(0). source
"},{"location":"research/analytical/#user-generated-content","title":"User Generated Content","text":"
  • Dongwook Shin, Stefano Vaccari, Assaf Zeevi (2022) Dynamic Pricing with Online Reviews. Management Science 0(0). source
  • Pu, Jingchuan, et al. \"Platform policies and sellers\u2019 competition in agency selling in the presence of online quality misrepresentation.\" Journal of Management Information Systems 39.1 (2022): 159-186. source
  • Shin, Dongwook, and Assaf Zeevi. \"Product quality and information sharing in the presence of reviews.\" Management Science (2023). https://doi.org/10.1287/mnsc.2023.4746
"},{"location":"research/analytical/#platform-openness","title":"Platform Openness","text":"
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"},{"location":"research/analytical/#contracting-and-moral-hazard","title":"Contracting and Moral Hazard","text":"
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"},{"location":"research/analytical/#recommendation-personalization","title":"Recommendation & Personalization","text":"
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"},{"location":"research/analytical/#competition","title":"Competition","text":""},{"location":"research/analytical/#price-competition","title":"Price Competition","text":"
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"},{"location":"research/analytical/#network","title":"Network","text":"

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"},{"location":"research/analytical/#information-sharing","title":"Information Sharing","text":"
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"},{"location":"research/analytical/#repeated-purchase","title":"Repeated Purchase","text":"
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"},{"location":"research/analytical/#counterfeits","title":"Counterfeits","text":"
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"},{"location":"research/analytical/#reputation","title":"Reputation","text":"
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"},{"location":"research/analytical/#ride-sharing","title":"Ride Sharing","text":"
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"},{"location":"research/analytical/#license","title":"License","text":"

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Last Update On 2023-04-15.

"},{"location":"research/empirical/","title":"Empirical Model","text":""},{"location":"research/empirical/#empirical-methodology","title":"Empirical Methodology","text":"
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"},{"location":"research/empirical/#advertising-and-recommendations","title":"Advertising and Recommendations","text":"
  • Kumar, Anuj, and Yinliang Tan. \"The demand effects of joint product advertising in online videos.\" Management Science 61.8 (2015): 1921-1937. source
  • Kumar, Anuj, and Kartik Hosanagar. \"Measuring the value of recommendation links on product demand.\" Information Systems Research 30.3 (2019): 819-838. source
  • Matthew McGranaghan, Jura Liaukonyte, Kenneth C. Wilbur (2022) How Viewer Tuning, Presence, and Attention Respond to Ad Content and Predict Brand Search Lift. Marketing Science 0(0). source
  • Adamopoulos, Panagiotis, Anindya Ghose, and Alexander Tuzhilin. \"Heterogeneous demand effects of recommendation strategies in a mobile application: Evidence from econometric models and machine-learning instruments.\" MIS Quarterly (2022). source
  • Tesary Lin, Sanjog Misra (2022) Frontiers: The Identity Fragmentation Bias. Marketing Science 0(0). source
  • Ada, S\u0131la, Nadia Abou Nabout, and Elea McDonnell Feit. \"EXPRESS: Context Information can Increase Revenue in Online Display Advertising Auctions: Evidence from a Policy Change.\" Journal of Marketing Research (2021). source
  • Rafieian, Omid, and Hema Yoganarasimhan. \u201cVariety Effects in Mobile Advertising.\u201d Journal of Marketing Research, Apr. 2022. source
  • Ghosh Dastidar, A., Sunder, S., & Shah, D. (2022). Societal Spillovers of TV Advertising: Social Distancing During a Public Health Crisis. Journal of Marketing, 0(0). source
  • Weijia Dai, Hyunjin Kim, Michael Luca (2023) Frontiers: Which Firms Gain from Digital Advertising? Evidence from a Field Experiment. Marketing Science 0(0). https://doi.org/10.1287/mksc.2023.1436
"},{"location":"research/empirical/#technology","title":"Technology","text":""},{"location":"research/empirical/#educational-technology","title":"Educational Technology","text":"
  • Kumar, Anuj, and Amit Mehra. \"Remedying Education with Personalized Homework: Evidence from a Randomized Field Experiment in India.\" Available at SSRN 2756059 (2018). source
  • Qiang Gao, Mingfeng Lin, D. J. Wu (2021) Education Crowdfunding and Student Performance: An Empirical Study. Information Systems Research 32(1):53-71. source
  • Samantha M. Keppler, Jun Li, Di (Andrew) Wu (2022) Crowdfunding the Front Lines: An Empirical Study of Teacher-Driven School Improvement. Management Science 0(0). source
"},{"location":"research/empirical/#green-technology","title":"Green Technology","text":"
  • Saldanha, Terence J. V.; Mithas, Sunil; Khuntia, Jiban; Whitaker, Jonathan; and Melville, Nigel P.. 2022. \"How Green Information Technology Standards and Strategies Influence Performance: Role of Environment, Cost, and Dual Focus,\" MIS Quarterly, (46: 4) pp.2367-2386. source
  • Zhiling Guo, Jin Li, Ram Ramesh (2023) Green Data Analytics of Supercomputing from Massive Sensor Networks: Does Workload Distribution Matter?. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1208
"},{"location":"research/empirical/#facial-recognition","title":"Facial Recognition","text":"
  • Jia Gao, Ying Rong, Xin Tian, Yuliang Yao (2023) Improving Convenience or Saving Face? An Empirical Analysis of the Use of Facial Recognition Payment Technology in Retail. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1205
"},{"location":"research/empirical/#voice","title":"Voice","text":"
  • Melzner, J., Bonezzi, A., & Meyvis, T. (2023). Information Disclosure in the Era of Voice Technology. Journal of Marketing, 0(0). https://doi.org/10.1177/00222429221138286
"},{"location":"research/empirical/#healthcare","title":"Healthcare","text":"
  • Elina H. Hwang, Xitong Guo, Yong Tan, Yuanyuan Dang (2022) Delivering Healthcare Through Teleconsultations: Implications for Offline Healthcare Disparity. Information Systems Research 0(0). source
  • Ginger Zhe Jin, Ajin Lee, Susan Feng Lu (2022) Patient Routing to Skilled Nursing Facilities: The Consequences of the Medicare Reimbursement Rule. Management Science 0(0). source
  • Ghose, Anindya, et al. \"Empowering patients using smart mobile health platforms: Evidence from a randomized field experiment.\" MIS Quarterly (2022). source
  • Clary, G., Dick, G., Akbulut, A. Y., & Van Slyke, C. (2022). The After Times: College Students\u2019 Desire to Continue with Distance Learning Post Pandemic. Communications of the Association for Information Systems, 50, pp-pp. source
  • Gorkem Turgut Ozer, Brad N. Greenwood, Anandasivam Gopal (2022) Digital Multisided Platforms and Women\u2019s Health: An Empirical Analysis of Peer-to-Peer Lending and Abortion Rates. Information Systems Research 0(0). source
  • Shelly Rathee, Kritika Narula, Arul Mishra, Himanshu Mishra (2022) Alphanumeric vs. Numeric Token Systems and the Healthcare Experience: Field Evidence from Healthcare Delivery in India. Management Science 0(0). source
  • Sykes, Tracy Ann, and Ruba Aljafari. \"We Are All in This Together, or Are We? Job Strain and Coping in the Context of an E-Healthcare System Implementation.\" Journal of Management Information Systems 39.4 (2022): 1215-1247. https://doi.org/10.1080/07421222.2022.2127450
  • Temidayo Adepoju, Anita L. Carson, Helen S. Jin, Christopher S. Manasseh (2023) Hospital Boarding Crises: The Impact of Urgent vs. Prevention Responses on Length of Stay. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4724
  • Sezgin Ayabakan, Indranil R. Bardhan, Zhiqiang (Eric) Zheng (2023) Impact of Telehealth and Process Virtualization on Healthcare Utilization. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1220
  • Thiebes, S., Gao, F., Briggs, R. O., Schmidt-Kraepelin, M., & Sunyaev, A. (2023). Design Concerns for Multiorganizational, Multistakeholder Collaboration: A Study in the Healthcare Industry. Journal of management information systems, 1. https://doi.org/10.1080/07421222.2023.2172771
"},{"location":"research/empirical/#pandemic","title":"Pandemic","text":"
  • Marta Serra-Garcia, Nora Szech (2022) Incentives and Defaults Can Increase COVID-19 Vaccine Intentions and Test Demand. Management Science 0(0). source
  • Joseph R. Buckman, Idris Adjerid, Catherine Tucker (2022) Privacy Regulation and Barriers to Public Health. Management Science 0(0). source
  • Jean-Philippe Bonardi, Quentin Gallea, Dimitrija Kalanoski, Rafael Lalive (2023) Managing Pandemics: How to Contain COVID-19 Through Internal and External Lockdowns and Their Release. Management Science 0(0). https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2022.4652
"},{"location":"research/empirical/#applications-of-artificial-intelligence","title":"Applications of Artificial Intelligence","text":"
  • Wang, Quan, Beibei Li, and Param Vir Singh. \"Copycats vs. original mobile apps: A machine learning copycat-detection method and empirical analysis.\" Information Systems Research 29.2 (2018): 273-291. source
  • Burtch, Gordon, Anindya Ghose, and Sunil Wattal. \"The hidden cost of accommodating crowdfunder privacy preferences: A randomized field experiment.\" Management Science 61.5 (2015): 949-962. source
  • V\u00edtor Albiero, and Kevin W. Bowyer. \"Is Face Recognition Sexist? No, Gendered Hairstyles and Biology Are\" BMVC 2020. source
  • Garvey, Aaron M., et al. \u201cBad News? Send an AI. Good News? Send a Human.\u201d Journal of Marketing, Feb. 2022. source
  • Martin Reisenbichler, Thomas Reutterer, David A. Schweidel, Daniel Dan (2022) Frontiers: Supporting Content Marketing with Natural Language Generation. Marketing Science 0(0). source
  • Andreas Barth, Sasan Mansouri, Fabian W\u00f6bbeking, (2022) \u201cLet Me Get Back to You\u201d\u2014A Machine Learning Approach to Measuring NonAnswers. Management Science 0(0). source
"},{"location":"research/empirical/#software","title":"Software","text":""},{"location":"research/empirical/#piracy","title":"Piracy","text":"
  • Martin Eisend. \"Explaining Digital Piracy: A Meta-Analysis.\" Information Systems Research 30.2 (2019): 636-664. source
  • Christian Peukert, Stefan Bechtold, Michail Batikas, Tobias Kretschmer (2022) Regulatory Spillovers and Data Governance: Evidence from the GDPR. Marketing Science 0(0). source
"},{"location":"research/empirical/#cybersecurity","title":"Cybersecurity","text":"
  • Kolini, F., & Janczewski, L. J. (2022). Exploring Incentives and Challenges for Cybersecurity Intelligence Sharing (CIS) across Organizations: A Systematic Review. Communications of the Association for Information Systems, 50, pp-pp. source
  • D'Arcy, John and Basoglu, Asli (2022) \"The Influences of Public and Institutional Pressure on Firms\u2019 Cybersecurity Disclosures,\" Journal of the Association for Information Systems, 23(3), 779-805. source
  • A. J. Burns, Tom L. Roberts, Clay Posey, Paul Benjamin Lowry, Bryan Fuller (2022) Going Beyond Deterrence: A Middle-Range Theory of Motives and Controls for Insider Computer Abuse. Information Systems Research 0(0). source
  • Nikkhah, Hamid Reza and Grover, Varun. 2022. \"An Empirical Investigation of Company Response to Data Breaches,\" MIS Quarterly, (46: 4) pp.2163-2196. source
"},{"location":"research/empirical/#electronic-participation","title":"Electronic Participation","text":"
  • Yo, Y., & Xu, P. (2022). The Power of Electronic Channels and Electronic Political Efficacy: Electronic Participation Discourse. Communications of the Association for Information Systems, 50, pp-pp. source
"},{"location":"research/empirical/#productivity","title":"Productivity","text":"
  • Peng Huang, Marco Ceccagnoli, Chris Forman, D.J. Wu (2022) IT Knowledge Spillovers, Absorptive Capacity, and Productivity: Evidence from Enterprise Software. Information Systems Research 0(0). source
"},{"location":"research/empirical/#software-development","title":"Software Development","text":"
  • Gregory Vial (2023) A Complex Adaptive Systems Perspective of Software Reuse in the Digital Age: An Agenda for IS Research. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1200
"},{"location":"research/empirical/#algorithm","title":"Algorithm","text":""},{"location":"research/empirical/#impact","title":"Impact","text":"
  • Athey, Susan. \"The impact of machine learning on economics.\" The economics of artificial intelligence: An agenda. University of Chicago Press, 2018. 507-547. pdf
"},{"location":"research/empirical/#bias","title":"Bias","text":"
  • Lambrecht, Anja, and Catherine Tucker. \"Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads.\" Management Science 65.7 (2019): 2966-2981. source
"},{"location":"research/empirical/#aversion","title":"Aversion","text":"
  • Dietvorst, Berkeley J., Joseph P. Simmons, and Cade Massey. \"Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them.\" Management Science 64.3 (2018): 1155-1170. source
  • Germann, Maximilian, and Christoph Merkle. \"Algorithm Aversion in Financial Investing.\" Available at SSRN 3364850 (2019). source
"},{"location":"research/empirical/#human-algorithm","title":"Human & Algorithm","text":"
  • Kleinberg, Jon, et al. \"Human decisions and machine predictions.\" The quarterly journal of economics 133.1 (2018): 237-293. source
  • Liwei Chen, J. J. Po-An Hsieh, Arun Rai (2022) How Does Intelligent System Knowledge Empowerment Yield Payoffs? Uncovering the Adaptation Mechanisms and Contingency Role of Work Experience. Information Systems Research 0(0). source
  • Tarafdar, Monideepa, Xinru Page, and Marco Marabelli. \"Algorithms as co\u2010workers: Human algorithm role interactions in algorithmic work.\" Information Systems Journal. source
  • Chen, Yang, et al. \"Does Techno-invasion Lead to Employees\u2019 Deviant Behaviors?.\" Journal of Management Information Systems 39.2 (2022): 454-482. source
  • You, Sangseok, Cathy Liu Yang, and Xitong Li. \"Algorithmic versus Human Advice: Does Presenting Prediction Performance Matter for Algorithm Appreciation?.\" Journal of Management Information Systems 39.2 (2022): 336-365. source
  • Ghasemaghaei, Maryam, and Ofir Turel. \"Why Do Data Analysts Take IT-Mediated Shortcuts? An Ego-Depletion Perspective.\" Journal of Management Information Systems 39.2 (2022): 483-512. source
  • Elizabeth Han, Dezhi Yin, Han Zhang (2022) Bots with Feelings: Should AI Agents Express Positive Emotion in Customer Service?. Information Systems Research 0(0). source
  • Mikhail Lysyakov , Siva Viswanathan (2022) Threatened by AI: Analyzing Users\u2019 Responses to the Introduction of AI in a Crowd-Sourcing Platform. Information Systems Research 0(0). source
  • Nasim Mousavi, Panagiotis Adamopoulos, Jesse Bockstedt (2023) The Decoy Effect and Recommendation Systems. Information Systems Research 0(0). https://doi.org/10.1287/isre.2022.1197
  • Callen Anthony, Beth A. Bechky, Anne-Laure Fayard (2023) \u201cCollaborating\u201d with AI: Taking a System View to Explore the Future of Work. Organization Science 0(0). https://doi.org/10.1287/orsc.2022.1651
  • Chandra, Shalini, Anuragini Shirish, and Shirish C. Srivastava. \"To Be or Not to Be\u2026 Human? Theorizing the Role of Human-Like Competencies in Conversational Artificial Intelligence Agents.\" Journal of Management Information Systems 39.4 (2022): 969-1005. https://doi.org/10.1080/07421222.2022.2127441
  • Tarafdar, Monideepa, Xinru Page, and Marco Marabelli. \"Algorithms as co\u2010workers: Human algorithm role interactions in algorithmic work.\" Information Systems Journal (2022). https://doi.org/10.1111/isj.12389
  • Kevin Bauer, Moritz von Zahn, Oliver Hinz (2023) Expl(AI)ned: The Impact of Explainable Artificial Intelligence on Users\u2019 Information Processing. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1199
  • Parker, Sara, and Derek Ruths. \"Is hate speech detection the solution the world wants?.\" Proceedings of the National Academy of Sciences 120.10 (2023): e2209384120. https://doi.org/10.1073/pnas.2209384120
  • Chandra, Shalini, Anuragini Shirish, and Shirish C. Srivastava. \"To Be or Not to Be\u2026 Human? Theorizing the Role of Human-Like Competencies in Conversational Artificial Intelligence Agents.\" Journal of Management Information Systems 39.4 (2022): 969-1005. https://doi.org/10.1080/07421222.2022.2127441
  • Boyac\u0131, Tamer, Caner Canyakmaz, and Francis de V\u00e9ricourt. \"Human and Machine: The Impact of Machine Input on Decision Making Under Cognitive Limitations.\" Management Science (2023). https://doi.org/10.1287/mnsc.2023.4744
  • Dolata, M., Katsiuba, D., Wellnhammer, N., & Schwabe, G. (2023). Learning with Digital Agents: An Analysis based on the Activity Theory. Journal of Management Information Systems, 40(1), 56-95. https://doi.org/10.1080/07421222.2023.2172775
"},{"location":"research/empirical/#gender","title":"Gender","text":"
  • Lin, Chen, et al. \"Do \"Little Emperors\u201d Get More Than \u201cLittle Empresses\"? Boy-Girl Gender Discrimination as Evidenced by Consumption Behavior of Chinese Households.\" Marketing Science (2021). source
  • Helena Fornwagner, Monika Pompeo, Nina Serdarevic (2022) Choosing Competition on Behalf of Someone Else. Management Science 0(0). source
  • Emilio J. Castilla, Hye Jin Rho (2023) The Gendering of Job Postings in the Online Recruitment Process. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4674
  • Eliot L. Sherman, Raina Brands, Gillian Ku (2023) Dropping Anchor: A Field Experiment Assessing a Salary History Ban with Archival Replication. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4658
  • Zhiyan Wu, Lucia Naldi, Karl Wennberg, Timur Uman (2023) Learning from Their Daughters: Family Exposure to Gender Disparity and Female Representation in Male-Led Ventures. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4727
"},{"location":"research/empirical/#privacy","title":"Privacy","text":"
  • Godinho de Matos, Miguel, and Idris Adjerid. \"Consumer consent and firm targeting after GDPR: The case of a large telecom provider.\" Management Science (2021). source
  • Heng Xu, Nan Zhang (2022) From Contextualizing to Context Theorizing: Assessing Context Effects in Privacy Research. Management Science 0(0). source
  • Karwatzki, Sabrina et al. The multidimensional nature of privacy risks: Conceptualisation, measurement and implications for digital services. Information Systems Journal (2022). source
  • Tesary Lin (2022) Valuing Intrinsic and Instrumental Preferences for Privacy. Marketing Science 0(0). source
  • Tawfiq Alashoor, Mark Keil, H. Jeff Smith, Allen R. McConnell (2022) Too Tired and in Too Good of a Mood to Worry About Privacy: Explaining the Privacy Paradox Through the Lens of Effort Level in Information Processing. Information Systems Research 0(0). source
  • Ram D. Gopal, Hooman Hidaji, Sule Nur Kutlu, Raymond A. Patterson, Niam Yaraghi (2023) Law, Economics, and Privacy: Implications of Government Policies on Website and Third-Party Information Sharing. Information Systems Research 0(0). https://doi.org/10.1287/isre.2022.1178
  • Garrett A. Johnson, Scott K. Shriver, Samuel G. Goldberg (2023) Privacy and Market Concentration: Intended and Unintended Consequences of the GDPR. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4709
"},{"location":"research/empirical/#online-platforms","title":"Online Platforms","text":""},{"location":"research/empirical/#subscription-models","title":"Subscription Models","text":"
  • Oestreicher-Singer, Gal, and Lior Zalmanson. \"Content or community? A digital business strategy for content providers in the social age.\" MIS quarterly (2013): 591-616. source
  • Bapna, Ravi, and Akhmed Umyarov. \"Do your online friends make you pay? A randomized field experiment on peer influence in online social networks.\" Management Science 61.8 (2015): 1902-1920. source
  • Hongfei Li, Jing Peng, Xinxin Li, Jan Stallaert (2022) When More Can Be Less: The Effect of Add-On Insurance on the Consumption of Professional Services. Information Systems Research 0(0). source
"},{"location":"research/empirical/#digital-content-user-generated-content","title":"Digital Content & User-Generated Content","text":"
  • Ye, Hua, et al. \"Monetization of Digital Content: Drivers of Revenue on Q&A Platforms.\" Journal of Management Information Systems 38.2 (2021): 457-483. source
  • Zhiyu Zeng, Hengchen Dai, Dennis J. Zhang, Heng Zhang, Renyu Zhang, Zhiwei Xu, Zuo-Jun Max Shen (2022) The Impact of Social Nudges on User-Generated Content for Social Network Platforms. Management Science 0(0). source
  • Lu, S., Dinner, I., & Grewal, R. (2023). The Ripple Effect of Firm-Generated Content on New Movie Releases. Journal of Marketing Research, 0(0). https://doi.org/10.1177/00222437221143066
"},{"location":"research/empirical/#online-reviews","title":"Online Reviews","text":"
  • Chen, Yan, et al. \"Social comparisons and contributions to online communities: A field experiment on movielens.\" American Economic Review 100.4 (2010): 1358-98. source
  • Gordon Burtch, Yili Hong, Ravi Bapna, Vladas Griskevicius (2017) Stimulating Online Reviews by Combining Financial Incentives and Social Norms. Management Science 64(5):2065-2082. source
  • Limin Fang (2022) The Effects of Online Review Platforms on Restaurant Revenue, Consumer Learning, and Welfare. Management Science 0(0). source
  • Jinghui (Jove) Hou, Xiao Ma (2022) Space Norms for Constructing Quality Reviews on Online Consumer Review Sites. Information Systems Research 0(0). source
  • Sherry He, Brett Hollenbeck, Davide Proserpio (2022) The Market for Fake Reviews. Marketing Science 0(0). source
  • Honglin Deng, Weiquan Wang, Siyuan Li, and Kai H. Lim. \"Can Positive Online Social Cues Always Reduce User Avoidance of Sponsored Search Results?.\" MIS Quarterly (2021). source
  • Mengxia Zhang, Lan Luo (2022) Can Consumer-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp. Management Science 0(0). source
  • Choi, HanByeol Stella, et al. \"Effects of Online Crowds on Self-Disclosure Behaviors in Online Reviews: A Multidimensional Examination.\" Journal of Management Information Systems 39.1 (2022): 218-246. source
  • T. Ravichandran, Chaoqun Deng (2022) Effects of Managerial Response to Negative Reviews on Future Review Valence and Complaints. Information Systems Research 0(0). source
  • Jung, M., Ryu, S., Han, S. P., & Cho, D. (2023). Ask for Reviews at the Right Time: Evidence from Two Field Experiments. Journal of Marketing, 0(0). https://doi.org/10.1177/00222429221143329
  • Chen, Y., & Lee, S. (2023). User-Generated Physician Ratings and Their Effects on Patients\u2019 Physician Choices: Evidence from Yelp. Journal of Marketing, 0(0). https://doi.org/10.1177/00222429221146511
  • Uttara Ananthakrishnan, Davide Proserpio, Siddhartha Sharma (2023) I Hear You: Does Quality Improve with Customer Voice?. Marketing Science 0(0). https://doi.org/10.1287/mksc.2023.1437
  • Andrey Fradkin, David Holtz (2023) Do Incentives to Review Help the Market? Evidence from a Field Experiment on Airbnb. Marketing Science 0(0). https://doi.org/10.1287/mksc.2023.1439
"},{"location":"research/empirical/#digital-beauty-filter","title":"Digital Beauty Filter","text":"
  • I asked an AI to tell me how beautiful I am
  • Beauty filters are changing the way young girls see themselves
  • TikTok changed the shape of some people\u2019s faces without asking
  • How digital beauty filters perpetuate colorism
  • Losh, Elizabeth. \"Selfies| Feminism Reads Big Data:\" Social Physics,\" Atomism, and Selfiecity.\" International Journal of Communication 9 (2015): 13. source
  • Elias, Ana Sofia, and Rosalind Gill. \"Beauty surveillance: The digital self-monitoring cultures of neoliberalism.\" European Journal of Cultural Studies 21.1 (2018): 59-77. source
"},{"location":"research/empirical/#platform-growth-merge-and-acquisition","title":"Platform Growth, Merge and Acquisition","text":"
  • Chiara Farronato, Jessica Fong, Andrey Fradkin (2023) Dog Eat Dog: Balancing Network Effects and Differentiation in a Digital Platform Merger. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4675
"},{"location":"research/empirical/#social-media","title":"Social Media","text":"
  • Xu, Haifeng, et al (2022) \"Why Are People Addicted to SNS? Understanding the Role of SNS Characteristics in the Formation of SNS Addiction,\" Journal of the Association for Information Systems, 23(3), 806-837. source
  • Peng, Jing, Juheng Zhang, and Ram Gopal. \"The Good, the Bad, and the Social Media: Financial Implications of Social Media Reactions to Firm-Related News.\" Journal of Management Information Systems 39.3 (2022): 706-732. source
"},{"location":"research/empirical/#interactions","title":"Interactions","text":"
  • Matook, Sabine, Alan R. Dennis, and Yazhu Maggie Wang. \"User comments in social media firestorms: A mixed-method study of purpose, tone, and motivation.\" Journal of Management Information Systems 39.3 (2022): 673-705. source
  • Lu, Yingda; Wu, Junjie; Tan, Yong; and Chen, Jian. 2022. \"Microblogging Replies and Opinion Polarization: A Natural Experiment,\" MIS Quarterly, (46: 4) pp.1901-1936. source
  • Yun Young Hur, Fujie Jin, Xitong Li, Yuan Cheng, Yu Jeffrey Hu (2022) Does Social Influence Change with Other Information Sources? A Large-Scale Randomized Experiment in Medical Crowdfunding. Information Systems Research 0(0). source
  • Wakefield, R. L., & Wakefield, K. (2022). The antecedents and consequences of intergroup affective polarisation on social media. Information Systems Journal, 1\u2013 29. source
  • Miller, Stacy, et al. \"Integrating truth bias and elaboration likelihood to understand how political polarisation impacts disinformation engagement on social media.\" Information Systems Journal (2022). source
  • Wang, Lin, Chong Wang, and Xinyan Yao. \"Befriended to polarise? The impact of friend identity on review polarisation\u2014A quasi\u2010experiment.\" Information Systems Journal. https://doi.org/10.1111/isj.12425
"},{"location":"research/empirical/#fake-news-on-social-media","title":"Fake News on Social Media","text":"
  • Wang, Shuting, Min-Seok Pang, and Paul A. Pavlou. \"Cure or Poison? Identity Verification and the Posting of Fake News on Social Media.\" Journal of Management Information Systems 38.4 (2021): 1011-1038. source
  • Horner, Christy Galletta, et al. \"Emotions: The Unexplored Fuel of Fake News on Social Media.\" Journal of Management Information Systems 38.4 (2021): 1039-1066. source
  • Deng, Bingjie, and Michael Chau. \"The Effect of the Expressed Anger and Sadness on Online News Believability.\" Journal of Management Information Systems 38.4 (2021): 959-988. source
  • Turel, Ofir, and Babajide Osatuyi. \"Biased Credibility and Sharing of Fake News on Social Media: Considering Peer Context and Self-Objectivity State.\" Journal of Management Information Systems 38.4 (2021): 931-958. source
  • Ng, Ka Chung, Jie Tang, and Dongwon Lee. \"The Effect of Platform Intervention Policies on Fake News Dissemination and Survival: An Empirical Examination.\" Journal of Management Information Systems 38.4 (2021): 898-930. source
  • George, Jordana, Natalie Gerhart, and Russell Torres. \"Uncovering the Truth about Fake News: A Research Model Grounded in Multi-Disciplinary Literature.\" Journal of Management Information Systems 38.4 (2021): 1067-1094. source
  • Gimpel, Henner, et al. \"The effectiveness of social norms in fighting fake news on social media.\" Journal of Management Information Systems 38.1 (2021): 196-221. source
  • Mohamed Mostagir, Asuman Ozdaglar, James Siderius (2022) When Is Society Susceptible to Manipulation?. Management Science 0(0). source
  • Jackie London Jr., Siyuan Li, Heshan Sun (2022) Seems Legit: An Investigation of the Assessing and Sharing of Unverifiable Messages on Online Social Networks. Information Systems Research 0(0). source
  • Mohamed Mostagir, James Siderius (2022) Learning in a Post-Truth World. Management Science 0(0). source
  • Wang, Shuting (Ada); Pang, Min-Seok; and Pavlou, Paul A.. 2022. \"Seeing Is Believing? How Including a Video in Fake News Influences Users\u2019 Reporting of Fake News to Social Media Platforms,\" MIS Quarterly, (46: 3) pp.1323-1354. source
  • Gizem Ceylan, Ian A. Anderson, and Wendy Wood. 2022. \"Sharing of misinformation is habitual, not just lazy or biased,\" PNAS, (120:4) https://doi.org/10.1073/pnas.2216614120
"},{"location":"research/empirical/#social-media-marketing","title":"Social Media Marketing","text":"
  • Tingting Nian, Arun Sundararajan (2022) Social Media Marketing, Quality Signaling, and the Goldilocks Principle. Information Systems Research 0(0). source
  • Jens Foerderer, Sebastian W. Schuetz (2022) Data Breach Announcements and Stock Market Reactions: A Matter of Timing?. Management Science 0(0). source
  • Naveen Kumar, Liangfei Qiu, Subodha Kumar (2022) A Hashtag Is Worth a Thousand Words: An Empirical Investigation of Social Media Strategies in Trademarking Hashtags. Information Systems Research 0(0). source
  • Venkatesan, Srikanth, et al. \"INFLUENCE IN SOCIAL MEDIA: AN INVESTIGATION OF TWEETS SPANNING THE 2011 EGYPTIAN REVOLUTION.\" MIS Quarterly 45.4 (2021). source
  • Alibakhshi, Reza, and Shirish C. Srivastava. \"Post-Story: Influence of Introducing Story Feature on Social Media Posts.\" Journal of Management Information Systems 39.2 (2022): 573-601. source
  • Weiler, Michael, et al. \" Social Capital Accumulation Through Social Media Networks: Evidence from a Randomized Field Experiment and Individual-Level Panel Data,\" Management Information Systems Quarterly, (2021). source
  • Leung, Fine F., et al. \"Influencer Marketing Effectiveness.\" Journal of Marketing (2022) source
  • Liadeli, G., Sotgiu, F., & Verlegh, P. W. J. (2022). A Meta-Analysis of the Effects of Brands\u2019 Owned Social Media on Social Media Engagement and Sales. Journal of Marketing, 0(0). source
  • Woolley, K., Kupor, D., & Liu, P. J. (2022). Does Company Size Shape Product Quality Inferences? Larger Companies Make Better High-Tech Products, but Smaller Companies Make Better Low-Tech Products. Journal of Marketing Research, 0(0). source
"},{"location":"research/empirical/#norms-and-roles","title":"Norms And Roles","text":"
  • Emmanuelle Vaast, Alain Pinsonneault (2022) Dealing with the Social Media Polycontextuality of Work. Information Systems Research 0(0). source
  • Verena Schoenmueller, Oded Netzer, Florian Stahl (2022) Frontiers: Polarized America: From Political Polarization to Preference Polarization. Marketing Science 0(0). source
"},{"location":"research/empirical/#social-investing","title":"Social Investing","text":"
  • Jake An, Donnel Briley, Shai Danziger, Shai Levi (2022) The Impact of Social Investing on Charitable Donations. Management Science 0(0). source
"},{"location":"research/empirical/#network-graph","title":"Network & Graph","text":"
  • Mariia Petryk, Michael Rivera, Siddharth Bhattacharya, Liangfei Qiu, Subodha Kumar (2022) How Network Embeddedness Affects Real-Time Performance Feedback: An Empirical Investigation. Information Systems Research 0(0). source
  • Rohit Aggarwal, Vishal Midha, Nicholas Sullivan (2023) Effect of Online Professional Network Recommendations on the Likelihood of an Interview: A Field Study. Information Systems Research 0(0). https://doi.org/10.1287/isre.2021.1053
  • Rohit Aggarwal, Vishal Midha, Nicholas Sullivan (2023) The Effect of Gender Expectations and Physical Attractiveness on Discussion of Weakness in Online Professional Recommendations. Information Systems Research 0(0). https://doi.org/10.1287/isre.2021.1032
"},{"location":"research/empirical/#content-consumption-sharing","title":"Content Consumption & Sharing","text":"
  • Hyelim Oh, Khim-Yong Goh, Tuan Q. Phan (2022) Are You What You Tweet? The Impact of Sentiment on Digital News Consumption and Social Media Sharing. Information Systems Research 0(0). source
  • Barnea, U., Meyer, R. J., & Nave, G. (2023). The Effects of Content Ephemerality on Information Processing. Journal of Marketing Research, 0(0). https://doi.org/10.1177/00222437221131047
"},{"location":"research/empirical/#news","title":"News","text":"
  • O\u2019Riordan, S., Emerson, B., Feller, J., & Kiely, G. (2023). The Road to Open News: A Theory of Social Signaling in an Open News Production Community. Journal of Management Information Systems, 40(1), 130-162. https://doi.org/10.1080/07421222.2023.2172777
"},{"location":"research/empirical/#e-commerce-online-shopping","title":"E-Commerce & Online Shopping","text":"
  • McKnight, D. Harrison, Vivek Choudhury, and Charles Kacmar. \"Developing and validating trust measures for e-commerce: An integrative typology.\" Information systems research 13.3 (2002): 334-359. source
  • Shang, Rong-An, Yu-Chen Chen, and Lysander Shen. \"Extrinsic versus intrinsic motivations for consumers to shop on-line.\" Information & management 42.3 (2005): 401-413. source
  • Kim, Hee-Woong, Hock Chuan Chan, and Atreyi Kankanhalli. \"What motivates people to purchase digital items on virtual community websites? The desire for online self-presentation.\" Information systems research 23.4 (2012): 1232-1245. source
  • Pavlou, Paul A. \"Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model.\" International journal of electronic commerce 7.3 (2003): 101-134. source
  • Arvind K. Tripathi, Young-Jin Lee, Amit Basu (2022) Analyzing the Impact of Public Buyer\u2013Seller Engagement During Online Auctions. Information Systems Research 0(0). source
  • Khan, A., & Krishnan, S. (2022). Ethical Behavior of Firms and B2C E-commerce Diffusion: Exploring the Mediating Roles of Customer Orientation and Innovation Capacity. Communications of the Association for Information Systems, 50, pp-pp. source
  • Iyengar R, Park Y-H, Yu Q. The Impact of Subscription Programs on Customer Purchases. Journal of Marketing Research. 2022. source
  • Yufeng Huang, Bart J. Bronnenberg (2022) Consumer Transportation Costs and the Value of E-Commerce: Evidence from the Dutch Apparel Industry. Marketing Science 0(0). source
  • Bei, Z., & Gielens, K. (2022). The One-Party Versus Third-Party Platform Conundrum: How Can Brands Thrive? Journal of Marketing, 0(0). source
  • Deng, Honglin; Wang, Weiquan; and Lim, Kai H.. 2022. \"Repairing Integrity-Based Trust Violations in Ascription Disputes for Potential E-Commerce Customers,\" MIS Quarterly, (46: 4) pp.1983-2014. source
  • Murat Unal, Young-Hoon Park (2023) Fewer Clicks, More Purchases. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4716
  • Daniel W. Elfenbein, Raymond Fisman, Brian McManus (2023) The Impact of Socioeconomic and Cultural Differences on Online Trade. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4681
  • Yue Guan, Yong Tan, Qiang Wei, Guoqing Chen (2023) When Images Backfire: The Effect of Customer-Generated Images on Product Rating Dynamics. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1201
  • Ren, Fei; Tan, Yong; and Wan, Fei. 2023. \"Know Your Firm: Managing Social Media Engagement to Improve Firm Sales Performance,\" MIS Quarterly, (47: 1) pp.227-262. https://aisel.aisnet.org/misq/vol47/iss1/10/
  • Genevi\u00e8ve Bassellier, Jui Ramaprasad (2023) All External Reference Prices Are Not the Same: How Magnitude, Source, and Fairness Shape Payment for Digital Goods. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1206
  • Ilya Morozov (2023) Measuring Benefits from New Products in Markets with Information Frictions. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4729
  • KC, R. P., Mak, V., & Ofek, E. (2023). Before or After? The Effects of Payment Decision Timing in Pay-What-You-Want Contexts. Journal of Marketing, 0(0). https://doi.org/10.1177/00222429221142234
"},{"location":"research/empirical/#crowdfunding-markets","title":"Crowdfunding Markets","text":"
  • Yan Xu, Jian Ni (2022) Entrepreneurial Learning and Disincentives in Crowdfunding Markets. Management Science 0(0). source
  • Kao, Ta-Wei, et al. \"Deriving Execution Effectiveness of Crowdfunding Projects from the Fundraiser Network.\" Journal of Management Information Systems 39.1 (2022): 276-301. source
  • Rhue, Lauren and Clark, Jessica. 2022. \"Who Are You and What Are You Selling? Creator-Based and Product-Based Racial Cues in Crowdfunding,\" MIS Quarterly, (46: 4) pp.2229-2260. source
  • Markus Weinmann, Abhay Nath Mishra, Lena Franziska Kaiser, Jan vom Brocke (2022) The Attraction Effect in Crowdfunding. Information Systems Research 0(0). source
  • Lin, Mingfeng, et al. \"# Experts vs. Non-Experts in Online Crowdfunding Markets.\" Management Information Systems Quarterly 47.1 (2023): 97-126. https://aisel.aisnet.org/misq/vol47/iss1/6
"},{"location":"research/empirical/#crowdsourcing","title":"Crowdsourcing","text":"
  • Cao, Fang, et al. \"Do Social Dominance-Based Faultlines Help or Hurt Team Performance in Crowdsourcing Tournaments?.\" Journal of Management Information Systems 39.1 (2022): 247-275. source
  • Deodhar, Swanand J.; Babar, Yash; and Burtch, Gordon. 2022. \"The Influence of Status on Evaluations: Evidence from Online Coding Contests,\" MIS Quarterly, (46: 4) pp.2085-2110. source
  • Yan, Bei, and Andrea B. Hollingshead. \"Motivating the Motivationally Diverse Crowd: Social Value Orientation and Reward Structure in Crowd Idea Generation.\" Journal of Management Information Systems 39.4 (2022): 1064-1088. https://doi.org/10.1080/07421222.2022.2127451
"},{"location":"research/empirical/#social-reputation","title":"Social Reputation","text":"
  • Ohanian, Roobina. \"Construction and validation of a scale to measure celebrity endorsers' perceived expertise, trustworthiness, and attractiveness.\" Journal of advertising 19.3 (1990): 39-52. source
  • Swanand J. Deodhar, Samrat Gupta (2022) The Impact of Social Reputation Features in Innovation Tournaments: Evidence from a Natural Experiment. Information Systems Research 0(0). source
  • David Keith, Lauren Taylor, James Paine, Richard Weisbach, Anthony Dowidowicz (2022) When Funders Aren\u2019t Customers: Reputation Management and Capability Underinvestment in Multiaudience Organizations. Organization Science 0(0). source
"},{"location":"research/empirical/#waiting-delays","title":"Waiting & Delays","text":"
  • Taylor, Shirley. \"Waiting for service: the relationship between delays and evaluations of service.\" Journal of marketing 58.2 (1994): 56-69. source
  • Dellaert, Benedict GC, and Barbara E. Kahn. \"How tolerable is delay?: Consumers\u2019 evaluations of internet web sites after waiting.\" Journal of interactive marketing 13.1 (1999): 41-54. source
  • Hoxmeier, John A., and Chris DiCesare. \"System response time and user satisfaction: An experimental study of browser-based applications.\" (2000). source
  • Weinberg, Bruce D. \"Don't keep your internet customers waiting too long at the (virtual) front door.\" Journal of interactive marketing 14.1 (2000): 30-39. source
  • Galletta, Dennis F., et al. \"Web site delays: How tolerant are users?.\" Journal of the Association for Information Systems 5.1 (2004): 1. source
  • Nah, Fiona Fui-Hoon. \"A study on tolerable waiting time: how long are web users willing to wait?.\" Behaviour & Information Technology 23.3 (2004): 153-163. source
  • Lee, Younghwa, Andrew NK Chen, and Virginia Ilie. \"Can online wait be managed? The effect of filler interfaces and presentation modes on perceived waiting time online.\" Mis Quarterly (2012): 365-394. source
"},{"location":"research/empirical/#word-of-mouth-and-behavior","title":"Word of Mouth And Behavior","text":"
  • Kunst, Katrine, Torsten Ringberg, and Ravi Vatrapu. \"Beyond popularity: A user perspective on observable behaviours in a digital platform.\" Information Systems Journal (2021). source
  • Tao Lu, May Yuan, Chong (Alex) Wang, Xiaoquan (Michael) Zhang (2022) Histogram Distortion Bias in Consumer Choices. Management Science 0(0). source
  • Sabzehzar, Amin, et al. \"Putting Religious Bias in Context: How Offline and Online Contexts Shape Religious Bias in Online Prosocial Lending.\" Management Information Systems Quarterly 47.1 (2023): 33-62. https://aisel.aisnet.org/misq/vol47/iss1/4
"},{"location":"research/empirical/#live-stream","title":"Live Stream","text":"
  • Guan, Zhengzhi, et al. \"What influences the purchase of virtual gifts in live streaming in China? A cultural context\u2010sensitive model.\" Information Systems Journal (2021). source
  • Sim, Jaeung, et al. \"In-Consumption Information Cues and Digital Content Demand: Evidence from a Live-Streaming Platform.\" Available at SSRN 3922723 (2021). source
  • Brett, Noel. \"Why Do We Only Get Anime Girl Avatars? Collective White Heteronormative Avatar Design in Live Streams.\" Television & New Media (2022): source
"},{"location":"research/empirical/#collaboration","title":"Collaboration","text":""},{"location":"research/empirical/#-li-he-chen-zhang-and-william-j-kettinger-digital-platform-ecosystem-dynamics-the-roles-of-product-scope-innovation-and-collaborative-network-centrality-mis-quarterly-462-2022-source","title":"- Li, He, Chen Zhang, and William J. Kettinger. \"DIGITAL PLATFORM ECOSYSTEM DYNAMICS: THE ROLES OF PRODUCT SCOPE, INNOVATION, AND COLLABORATIVE NETWORK CENTRALITY.\" MIS Quarterly 46.2 (2022). source","text":""},{"location":"research/empirical/#communities","title":"Communities","text":"
  • Li, Yang-Jun; Cheung, Christy M.K; Shen, Xiao-Liang; and Lee, Matthew K. O. (2022) \"When Socialization Goes Wrong: Understanding the We-Intention to Participate in Collective Trolling in Virtual Communities,\" Journal of the Association for Information Systems, 23(3), 678-706. source
  • Zhou, Jiaqi, et al. \"Unintended Emotional Effects of Online Health Communities: A Text Mining-Supported Empirical Study.\" Management Information Systems Quarterly 47.1 (2023): 195-226. https://aisel.aisnet.org/misq/vol47/iss1/9
"},{"location":"research/empirical/#online-dating","title":"Online Dating","text":"
  • Ravi Bapna, Edward McFowland, III, Probal Mojumder, Jui Ramaprasad, Akhmed Umyarov (2022) So, Who Likes You? Evidence from a Randomized Field Experiment. Management Science 0(0). source
"},{"location":"research/empirical/#generativity","title":"Generativity","text":"
  • Daniel F\u00fcrstenau, Abayomi Baiyere, Kai Schewina, Matthias Schulte-Althoff, Hannes Rothe (2023) Extended Generativity Theory on Digital Platforms. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1209
"},{"location":"research/empirical/#video-games","title":"Video Games","text":"
  • Michael, David R., and Sandra L. Chen. Serious games: Games that educate, train, and inform. Muska & Lipman/Premier-Trade, 2005.
  • Le Wang, Yongqiang Sun, Xin (Robert) Luo. (2022) \"Game affordance, gamer orientation, and in-game purchases: A hedonic\u2013instrumental framework,\" Information Systems Journal. source
  • Le Wang, Paul Benjamin Lowry, Xin (Robert) Luo, Han Li (2022) Moving Consumers from Free to Fee in Platform-Based Markets: An Empirical Study of Multiplayer Online Battle Area Games. Information Systems Research 0(0). source
"},{"location":"research/empirical/#gamification","title":"Gamification","text":"
  • Zichermann, Gabe, and Christopher Cunningham. Gamification by design: Implementing game mechanics in web and mobile apps. \" O'Reilly Media, Inc.\", 2011.
  • Huotari, Kai, and Juho Hamari. \"Defining gamification: a service marketing perspective.\" Proceeding of the 16th international academic MindTrek conference. 2012. source
  • Hamari, Juho, Jonna Koivisto, and Harri Sarsa. \"Does gamification work?--a literature review of empirical studies on gamification.\" 2014 47th Hawaii international conference on system sciences. Ieee, 2014. source
  • Seaborn, Katie, and Deborah I. Fels. \"Gamification in theory and action: A survey.\" International Journal of human-computer studies 74 (2015): 14-31. source
  • Koivisto, Jonna, and Juho Hamari. \"The rise of motivational information systems: A review of gamification research.\" International Journal of Information Management 45 (2019): 191-210. source
  • Behnaz Bojd, Xiaolong Song, Yong Tan, Xiangbin Yan (2022) Gamified Challenges in Online Weight-Loss Communities. Information Systems Research 0(0). source
  • Kwak, Dong-Heon; Deng, Shuyuan; Kuem, Jungwon; and Kim, Sung S. (2022) \"How to Achieve Goals in Digital Games: An Empirical Test of a Goal-Oriented Model in Pok\u00e9mon GO,\" Journal of the Association for Information Systems, 23(2), 553-588. source
  • Alvin Chung Man Leung, Radhika Santhanam, Ron Chi-Wai Kwok, Wei Thoo Yue (2022) Could Gamification Designs Enhance Online Learning Through Personalization? Lessons from a Field Experiment. Information Systems Research 0(0). source
  • Jensen, Matthew L., et al. \"Improving Phishing Reporting Using Security Gamification.\" Journal of Management Information Systems 39.3 (2022): 793-823. source
  • Muhammad Zia Hydari, Idris Adjerid, Aaron D. Striegel (2022) Health Wearables, Gamification, and Healthful Activity. Management Science 0(0). source
"},{"location":"research/empirical/#video-game-live-streaming","title":"Video Game Live-streaming","text":"
  • Li, Yi, Chongli Wang, and Jing Liu. \"A systematic review of literature on user behavior in video game live streaming.\" International Journal of Environmental Research and Public Health 17.9 (2020): 3328. source
  • Simon Br\u00fcndl, Christian Matt, Thomas Hess & Simon Engert (2022) How Synchronous Participation Affects the Willingness to Subscribe to Social Live Streaming Services: The Role of Co-Interactive Behavior on Twitch, European Journal of Information Systems source
"},{"location":"research/empirical/#video-games-mental-health","title":"Video Games & Mental Health","text":"
  • Cheng, Zhi, Brad N. Greenwood, and Paul A. Pavlou. \"Location-based mobile gaming and local depression trends: a study of Pok\u00e9mon Go.\" Journal of Management Information Systems 39.1 (2022): 68-101. source
"},{"location":"research/empirical/#peer-influence","title":"Peer Influence","text":"
  • Jung, JaeHwuen, et al. \"Words Matter! Toward a Prosocial Call-to-Action for Online Referral: Evidence from Two Field Experiments.\" Information Systems Research (2020). source
  • Bryan Bollinger, Kenneth Gillingham, A. Justin Kirkpatrick, Steven Sexton (2022) Visibility and Peer Influence in Durable Good Adoption. Marketing Science 0(0). source
  • Rodrigo Belo, Ting Li (2022) Social Referral Programs for Freemium Platforms. Management Science 0(0). source
  • Pyo T-H, Lee JY, Park HM. The Effects of Consumer Preference and Peer Influence on Trial of an Experience Good. Journal of Marketing Research. May 2022. source
"},{"location":"research/empirical/#consumer-preference","title":"Consumer Preference","text":"
  • Pao-Li Chang, Tomoki Fujii, Wei Jin (2022) Good Names Beget Favors: The Impact of Country Image on Trade Flows and Welfare. Management Science 0(0). source
  • Andrew Meyer, Sean Hundtofte (2023) The Longshot Bias Is a Context Effect. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4684
"},{"location":"research/empirical/#labor-market","title":"Labor & Market","text":""},{"location":"research/empirical/#labor-supply","title":"Labor Supply","text":"
  • Hai Long Duong, Junhong Chu, Dai Yao (2022) Taxi Drivers\u2019 Response to Cancellations and No-Shows: New Evidence for Reference-Dependent Preferences. Management Science 0(0). source
  • Mithas, Sunil; Chen, Yanzhen; Liu, Che-Wei; and Han, Kunsoo. 2022. \"Are Foreign and Domestic Information Technology Professionals Complements or Substitutes?,\" MIS Quarterly, (46: 4) pp.2351-2366. source
  • Luxi Shen, Samuel D. Hirshman (2022) As Wages Increase, Do People Work More or Less? A Wage Frame Effect. Management Science 0(0). source
  • Sudheer Chava, Alexander Oettl, Manpreet Singh (2023) Does a One-Size-Fits-All Minimum Wage Cause Financial Stress for Small Businesses?. Management Science 0(0). source
  • Mitch Downey, Nelson Lind, Jeffrey G. Shrader (2023) Adjusting to Rain Before It Falls. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4697
"},{"location":"research/empirical/#work-schedule-improvement","title":"Work Schedule Improvement","text":"
  • Saravanan Kesavan, Susan J. Lambert, Joan C. Williams, Pradeep K. Pendem (2022) Doing Well by Doing Good: Improving Retail Store Performance with Responsible Scheduling Practices at the Gap, Inc.. Management Science 0(0). source
"},{"location":"research/empirical/#gig-economy","title":"Gig Economy","text":"
  • Yanhui Wu, Feng Zhu (2022) Competition, Contracts, and Creativity: Evidence from Novel Writing in a Platform Market. Management Science 0(0). source
"},{"location":"research/empirical/#conflict-with-work","title":"Conflict with Work","text":"
  • Massimo Magni, Manju K. Ahuja, Chiara Trombini (2022) Excessive Mobile Use and Family-Work Conflict: A Resource Drain Theory Approach to Examine Their Effects on Productivity and Well-Being. Information Systems Research 0(0). source
"},{"location":"research/empirical/#cyberloafing","title":"Cyberloafing","text":"
  • Qian Chen, et al (2022) How mindfulness decreases cyberloafing at work: a dual-system theory perspective, European Journal of Information Systems. source
"},{"location":"research/empirical/#career","title":"Career","text":"
  • Deng, X., Zaza, S., & Armstrong, D. J. (2023). What Motivates First-generation College Students to Consider an IT Career? An Integrative Perspective. Communications of the Association for Information Systems, 52, pp-pp. source
"},{"location":"research/empirical/#wage","title":"Wage","text":"
  • Sumit Agarwal, Meghana Ayyagari, Ren\u00e1ta Kosov\u00e1 (2023) Minimum Wage Increases and Employer Performance: Role of Employer Heterogeneity. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4650
"},{"location":"research/empirical/#service-operations","title":"Service Operations","text":"
  • Andres Musalem, Marcelo Olivares, Daniel Yung (2023) Balancing Agent Retention and Waiting Time in Service Platforms. Operations Research 0(0). https://doi.org/10.1287/opre.2022.2418
"},{"location":"research/empirical/#matching-markets","title":"Matching Markets","text":"
  • Lanfei Shi, Siva Viswanathan (2023) Optional Verification and Signaling in Online Matching Markets: Evidence from a Randomized Field Experiment. Information Systems Research 0(0). https://doi.org/10.1287/isre.2022.1194
"},{"location":"research/empirical/#addiction","title":"Addiction","text":"
  • Isaac Vaghefi, Bogdan Negoita, Liette Lapointe (2022) The Path to Hedonic Information System Use Addiction: A Process Model in the Context of Social Networking Sites. Information Systems Research 0(0). source
"},{"location":"research/empirical/#abuse","title":"Abuse","text":"
  • Amo, Laura C,; Grijalva, Emily; Herath, Tejaswini; Lemoine, G. James; and Rao, H. Raghav. 2022. \"Technological Entitlement: It\u2019s My Technology and I\u2019ll (Ab)Use It How I Want To,\" MIS Quarterly, (46: 3) pp.1395-1420. source
"},{"location":"research/empirical/#bayesian-belief","title":"Bayesian Belief","text":"
  • Markus M. M\u00f6bius, Muriel Niederle, Paul Niehaus, Tanya S. Rosenblat (2022) Managing Self-Confidence: Theory and Experimental Evidence. Management Science 0(0). source
  • Stefanie Brilon, Simona Grassi, Manuel Grieder, Jonathan F. Schulz (2023) Strategic Competition and Self-Confidence. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4688
"},{"location":"research/empirical/#copyright","title":"Copyright","text":"
  • Jeremy Watson, Megan MacGarvie, John McKeon (2022) It Was 50 Years Ago Today: Recording Copyright Term and the Supply of Music. Management Science 0(0). source
"},{"location":"research/empirical/#sports","title":"Sports","text":"
  • Bouke Klein Teeselink, Martijn J. van den Assem, Dennie van Dolder (2022) Does Losing Lead to Winning? An Empirical Analysis for Four Sports. Management Science 0(0). source
"},{"location":"research/empirical/#financial-technology-fintech","title":"Financial Technology (Fintech)","text":"
  • Christoph Herpfer, Aksel Mj\u00f8s, Cornelius Schmidt (2022) The Causal Impact of Distance on Bank Lending. Management Science 0(0). source
  • Ng, Evelyn, et al. \"The strategic options of fintech platforms: An overview and research agenda.\" Information Systems Journal (2022). https://doi.org/10.1111/isj.12388
  • Maggie Rong Hu, Xiaoyang Li, Yang Shi, Xiaoquan (Michael) Zhang (2023) Numerological Heuristics and Credit Risk in Peer-to-Peer Lending. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1202
"},{"location":"research/empirical/#decision-making","title":"Decision Making","text":"
  • Joshua Lewis, Daniel Feiler, Ron Adner (2022) The Worst-First Heuristic: How Decision Makers Manage Conjunctive Risk. Management Science 0(0). source
  • Huang, L., & Savary, J. (2022). When Payments Go Social: The Use\u00a0 of Person-to-Person Payment Methods Attenuates the Endowment Effect. Journal of Marketing Research, 0(0). source
  • Elif Incekara-Hafalir, Grace H. Y. Lee, Audrey K. L. Siah, Erte Xiao (2023) Incentives to Persevere. Management Science 0(0). source
  • Carlos Al\u00f3s-Ferrer, Michele Garagnani (2023) Part-Time Bayesians: Incentives and Behavioral Heterogeneity in Belief Updating. Management Science 0(0). source
  • Geoffrey Fisher (2023) Measuring the Factors Influencing Purchasing Decisions: Evidence From Cursor Tracking and Cognitive Modeling. Management Science 0(0). source
  • Steffen K\u00fcnn, Juan Palacios, Nico Pestel (2023) Indoor Air Quality and Strategic Decision Making. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4643
  • Fadong Chen, Zhi Zhu, Qiang Shen, Ian Krajbich, Todd A. Hare (2023) Intrachoice Dynamics Shape Social Decisions. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4732
"},{"location":"research/empirical/#team","title":"Team","text":"
  • Kilcullen, Molly, et al. \"Does Team Orientation Matter? A State of the Science Review, Meta\u2010Analysis and Multilevel Framework.\" Journal of Organizational Behavior. source
  • Fang, Yulin, Derrick Neufeld, and Xiaojie Zhang. \"Knowledge coordination via digital artefacts in highly dispersed teams.\" Information Systems Journal (2021). source
  • Dennis, Alexander S., Jordan B. Barlow, and Alan R. Dennis. \"The Power of Introverts: Personality and Intelligence in Virtual Teams.\" Journal of Management Information Systems 39.1 (2022): 102-129. source
  • Kearney, E, Razinskas, S, Weiss, M, Hoegl, M. Gender Diversity and Team Performance Under Time Pressure: The Role of Team Withdrawal and Information Elaboration. J Organ Behav. 2022. source
  • Lorens A. Imhof, Matthias Kr\u00e4kel (2022) Team Diversity and Incentives. Management Science 0(0). source
  • Tat Y. Chan, Yijun Chen, Chunhua Wu (2022) Collaborate to Compete: An Empirical Matching Game Under Incomplete Information in Rank-Order Tournaments. Marketing Science 0(0). source
  • Mullins, Jeffrey K. and Sabherwal, Rajiv. 2022. \"Just Enough Information? The Contingent Curvilinear Effect of Information Volume on Decision Performance in IS-Enabled Teams,\" MIS Quarterly, (46: 4) pp.2197-2228. source
"},{"location":"research/empirical/#sharing-economy","title":"Sharing Economy","text":"
  • John Tripp, D. Harrison McKnight & Nancy Lankton (2022) What most influences consumers\u2019 intention to use? different motivation and trust stories for uber, airbnb, and taskrabbit, European Journal of Information Systems source
  • Lee, Kyunghee; Jin, Qianran (Jenny); Animesh, Animesh; and Ramaprasad, Jui. 2022. \"Impact of Ride-Hailing Services on Transportation Mode Choices: Evidence from Traffic and Transit Ridership,\" MIS Quarterly, (46: 4) pp.1875-1900. source
  • Hyuck David Chung, Yue Maggie Zhou, Sendil Ethiraj (2023) Platform Governance in the Presence of Within-Complementor Interdependencies: Evidence from the Rideshare Industry. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4706
  • Yingjie Zhang, Beibei Li, Sean Qian (2023) Ridesharing and Digital Resilience for Urban Anomalies: Evidence from the New York City Taxi Market. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1212
"},{"location":"research/empirical/#is-use","title":"IS Use","text":"
  • Weinert, Christoph, et al. \"Repeated IT Interruption: Habituation and Sensitization of User Responses.\" Journal of Management Information Systems 39.1 (2022): 187-217. source
  • Park, Eun Hee, et al. \"Why do Family Members Reject AI in Health Care? Competing Effects of Emotions.\" Journal of Management Information Systems 39.3 (2022): 765-792. source
"},{"location":"research/empirical/#auction","title":"Auction","text":"
  • Yuexin Li, Xiaoyin Ma, Luc Renneboog (2022) In Art We Trust. Management Science 0(0). source
"},{"location":"research/empirical/#anti-corruption","title":"Anti-corruption","text":"
  • Lily Fang, Josh Lerner, Chaopeng Wu, Qi Zhang (2022) Anticorruption, Government Subsidies, and Innovation: Evidence from China. Management Science 0(0). source
"},{"location":"research/empirical/#visual","title":"Visual","text":"
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"},{"location":"research/empirical/#versioning","title":"Versioning","text":"
  • Yiting Deng, Anja Lambrecht, Yongdong Liu (2022) Spillover Effects and Freemium Strategy in the Mobile App Market. Management Science 0(0). source
"},{"location":"research/empirical/#loyalty-program","title":"Loyalty Program","text":"
  • Federico Rossi, Pradeep K. Chintagunta (2022) Consumer Loyalty Programs and Retail Prices: Evidence from Gasoline Markets. Marketing Science 0(0). source
"},{"location":"research/empirical/#promotion","title":"Promotion","text":"
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"},{"location":"research/empirical/#customization","title":"Customization","text":"
  • Fuchs, M., & Schreier, M. (2023). Paying Twice for Aesthetic Customization? The Negative Effect of Uniqueness on a Product\u2019s Resale Value. Journal of Marketing Research, 0(0). source
"},{"location":"research/empirical/#blockchain","title":"Blockchain","text":"
  • Xia Chen, Qiang Cheng, Ting Luo (2023) The Economic Value of Blockchain Applications: Early Evidence from Asset-Backed Securities. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4671
"},{"location":"research/empirical/#donation","title":"Donation","text":"
  • Waites, S. F., Farmer, A., Hasford, J., & Welden, R. (2023). Teach a Man to Fish: The Use of Autonomous Aid in Eliciting Donations. Journal of Marketing Research, 0(0). https://doi.org/10.1177/00222437221140028
"},{"location":"research/empirical/#immigration","title":"Immigration","text":"
  • Britta Glennon (2023) How Do Restrictions on High-Skilled Immigration Affect Offshoring? Evidence from the H-1B Program. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4715
"},{"location":"research/empirical/#production-and-collaboration","title":"Production and Collaboration","text":"
  • Abhishek Deshmane, Victor Mart\u00ednez-de-Alb\u00e9niz (2023) Come Together, Right Now? An Empirical Study of Collaborations in the Music Industry. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4743
"},{"location":"research/empirical/#education","title":"Education","text":"
  • Mingyu Chen (2023) The Value of U.S. College Education in Global Labor Markets: Experimental Evidence from China. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4745
"},{"location":"research/empirical/#it-systems","title":"IT Systems","text":"
  • Amrit Tiwana, Hani Safadi (2023) Atrophy in Aging Systems: Evidence, Dynamics, and Antidote. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1218
"},{"location":"research/empirical/#license","title":"License","text":"

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Last Update On 2023-04-15.

"},{"location":"research/technical/","title":"Technical Model / Design Science","text":""},{"location":"research/technical/#on-design-science","title":"On Design Science","text":"
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"},{"location":"research/technical/#label-imbalance","title":"Label Imbalance","text":"
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"},{"location":"research/technical/#label-noise","title":"Label Noise","text":"
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"},{"location":"research/technical/#natural-language-processing","title":"Natural Language Processing","text":""},{"location":"research/technical/#text-summarization","title":"Text Summarization","text":"
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"},{"location":"research/technical/#topic-modeling","title":"Topic Modeling","text":"
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"},{"location":"research/technical/#personality-measurement","title":"Personality Measurement","text":"
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"},{"location":"research/technical/#adversaries","title":"Adversaries","text":"
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"},{"location":"research/technical/#application","title":"Application","text":"
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"},{"location":"research/technical/#conversational-agents","title":"Conversational Agents","text":"
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"},{"location":"research/technical/#graph-embedding","title":"Graph Embedding","text":"
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"},{"location":"research/technical/#graphical-causality","title":"Graphical Causality","text":"
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"},{"location":"research/technical/#influence-maximization","title":"Influence Maximization","text":"
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"},{"location":"research/technical/#vertical-markets","title":"Vertical Markets","text":"
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"},{"location":"research/technical/#network-structures","title":"Network Structures","text":"
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"},{"location":"research/technical/#network-privacy","title":"Network Privacy","text":"
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"},{"location":"research/technical/#recommendation-systems","title":"Recommendation Systems","text":""},{"location":"research/technical/#recommendation-objectives","title":"Recommendation Objectives","text":"
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"},{"location":"research/technical/#dataset","title":"Dataset","text":"
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"},{"location":"research/technical/#link-prediction","title":"Link Prediction","text":"
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"},{"location":"research/technical/#recommendation-framework","title":"Recommendation Framework","text":"
  • Anelli, Vito Walter, et al. \"Elliot: a comprehensive and rigorous framework for reproducible recommender systems evaluation.\" Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2021. source code
  • TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale recommender systems (RecSys). It allows authors to train models with large embedding tables sharded across many GPUs.
  • Spotlight uses PyTorch to build both deep and shallow recommender models. By providing both a slew of building blocks for loss functions (various pointwise and pairwise ranking losses), representations (shallow factorization representations, deep sequence models), and utilities for fetching (or generating) recommendation datasets, it aims to be a tool for rapid exploration and prototyping of new recommender models.
  • MTReclib provides a PyTorch implementation of multi-task recommendation models and common datasets.
  • The repository microsoft/recommenders contains examples and best practices for building recommendation systems, provided as Jupyter notebooks.
  • Surprise is a Python scikit for building and analyzing recommender systems that deal with explicit rating data.
  • The repository hiroyuki-kasai/NMFLibrary is a pure-Matlab library of a collection of algorithms of non-negative matrix factorization (NMF).
  • QMF is a fast and scalable C++ library for implicit-feedback matrix factorization models (WALS and BPR).
  • The repository benfred/implicit provides fast Python implementations of several different popular recommendation algorithms for implicit feedback datasets.
  • This repository liu-yihong/BPRH implements the Bayesian personalized ranking method for heterogeneous implicit feedback.
  • reXmeX is recommender system evaluation metric library. It consists of utilities for recommender system evaluation. First, it provides a comprehensive collection of metrics for the evaluation of recommender systems. Second, it includes a variety of methods for reporting and plotting the performance results. Implemented metrics cover a range of well-known metrics and newly proposed metrics from data mining conferences and prominent journals.
"},{"location":"research/technical/#sequential-recommendation-systems","title":"Sequential Recommendation Systems","text":"
  • Quadrana, Massimo, Paolo Cremonesi, and Dietmar Jannach. \"Sequence-aware recommender systems.\" ACM Computing Surveys (CSUR) 51.4 (2018): 1-36. source
  • Maher, Mohamed, et al. \"Comprehensive Empirical Evaluation of Deep Learning Approaches for Session-based Recommendation in E-Commerce.\" arXiv preprint arXiv:2010.12540 (2020). source
  • Fang, Hui, et al. \"Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations.\" ACM Transactions on Information Systems (TOIS) 39.1 (2020): 1-42. source
  • Latifi, Sara, Noemi Mauro, and Dietmar Jannach. \"Session-aware recommendation: A surprising quest for the state-of-the-art.\" Information Sciences 573 (2021): 291-315. source
  • Wang, Shoujin, et al. \"A survey on session-based recommender systems.\" ACM Computing Surveys (CSUR) 54.7 (2021): 1-38. source
  • Wen Wang, Beibei Li, Xueming Luo, Xiaoyi Wang (2022) Deep Reinforcement Learning for Sequential Targeting. Management Science 0(0). source
  • Omid Rafieian (2022) Optimizing User Engagement Through Adaptive Ad Sequencing. Marketing Science 0(0). source
  • Yifu Li, Christopher Thomas Ryan, Lifei Sheng (2023) Optimal Sequencing in Single-Player Games. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4665
  • Marios Kokkodis, Panagiotis G. Ipeirotis (2023) The Good, the Bad, and the Unhirable: Recommending Job Applicants in Online Labor Markets. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4690
"},{"location":"research/technical/#user-item-matrix-factorization","title":"User-Item Matrix Factorization","text":"
  • Su, Xiaoyuan, and Taghi M. Khoshgoftaar. \"A survey of collaborative filtering techniques.\" Advances in artificial intelligence 2009 (2009). source
  • Cacheda, Fidel, et al. \"Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems.\" ACM Transactions on the Web (TWEB) 5.1 (2011): 1-33. source
  • Shi, Yue, Martha Larson, and Alan Hanjalic. \"Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges.\" ACM Computing Surveys (CSUR) 47.1 (2014): 1-45. source
  • Han, Soyeon Caren, et al. \"GLocal-K: Global and Local Kernels for Recommender Systems.\" Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2021. source
  • Rendle, Steffen, et al. \"Neural collaborative filtering vs. matrix factorization revisited.\" Fourteenth ACM conference on recommender systems. 2020. source
"},{"location":"research/technical/#graph-neural-network-based-recommendation","title":"Graph Neural Network Based Recommendation","text":"
  • Wu, Shiwen, et al. \"Graph neural networks in recommender systems: a survey.\" arXiv preprint arXiv:2011.02260 (2020). source
"},{"location":"research/technical/#reinforcement-learning-based-recommendation","title":"Reinforcement Learning Based Recommendation","text":"
  • Lin, Yuanguo, et al. \"A Survey on Reinforcement Learning for Recommender Systems.\" arXiv preprint arXiv:2109.10665 (2021). source
"},{"location":"research/technical/#causal-learning","title":"Causal Learning","text":"
  • Si, Zihua et al. \u201cA Model-Agnostic Causal Learning Framework for Recommendation using Search Data.\u201d (2022). source code
"},{"location":"research/technical/#self-supervised-learning_1","title":"Self-Supervised Learning","text":"
  • Yu, Junliang, et al. \"Self-Supervised Learning for Recommender Systems: A Survey.\" arXiv preprint arXiv:2203.15876 (2022). source
"},{"location":"research/technical/#debias","title":"Debias","text":"
  • Schnabel, Tobias, et al. \"Recommendations as treatments: Debiasing learning and evaluation.\" international conference on machine learning. PMLR, 2016. source
  • Chen, Jiawei, et al. \"AutoDebias: Learning to Debias for Recommendation.\" arXiv preprint arXiv:2105.04170 (2021). source
  • Jiawei Chen on github.com provides a repository at jiawei-chen/RecDebiasing
"},{"location":"research/technical/#user-reviews-for-recommendation","title":"User Reviews for Recommendation","text":"
  • Sachdeva, Noveen, and Julian McAuley. \"How useful are reviews for recommendation? a critical review and potential improvements.\" Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020. source
"},{"location":"research/technical/#regulations","title":"Regulations","text":"
  • Tommaso Di Noia, et al. 2022. Recommender systems under European AI regulations. Commun. ACM 65, 4 (April 2022), 69\u201373. source
"},{"location":"research/technical/#healthcare","title":"Healthcare","text":"
  • Ali Hajjar, Oguzhan Alagoz (2022) Personalized Disease Screening Decisions Considering a Chronic Condition. Management Science 0(0). source
  • Xiang Hui, Zekun Liu, Weiqing Zhang (2023) From High Bar to Uneven Bars: The Impact of Information Granularity in Quality Certification. Management Science 0(0). https://pubsonline.informs.org/doi/abs/10.1287/isre.2022.1191
  • Josh C. D\u2019Aeth, Shubhechyya Ghosal, Fiona Grimm, David Haw, Esma Koca, Krystal Lau, Huikang Liu, Stefano Moret, Dheeya Rizmie, Peter C. Smith, Giovanni Forchini, Marisa Miraldo, Wolfram Wiesemann (2023) Optimal Hospital Care Scheduling During the SARS-CoV-2 Pandemic. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4679
  • Johnson, M., Murthy, D., Robertson, B. W., Smith, W. R., & Stephens, K. K. (2023). Moving Emergency Response Forward: Leveraging Machine-Learning Classification of Disaster-Related Images Posted on Social Media. Journal of Management Information Systems, 40(1), 163-182. https://doi.org/10.1080/07421222.2023.2172778
"},{"location":"research/technical/#point-of-interest","title":"Point-of-Interest","text":"
  • Xiao-Jun Wang, Tao Liu, Weiguo Fan (2023) TGVx: Dynamic Personalized POI Deep Recommendation Model. INFORMS Journal on Computing 0(0). https://doi.org/10.1287/ijoc.2023.1286
"},{"location":"research/technical/#explainable-recommendation","title":"Explainable Recommendation","text":"
  • Zhang, Yongfeng, and Xu Chen. \"Explainable recommendation: A survey and new perspectives.\" Foundations and Trends in Information Retrieval 14.1 (2020): 1-101. source
  • Chen, Xu, Yongfeng Zhang, and Ji-Rong Wen. \"Measuring\" Why\" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendation.\" arXiv preprint arXiv:2202.06466 (2022). source
"},{"location":"research/technical/#attacking-recommendation-systems","title":"Attacking Recommendation Systems","text":"
  • Su, Xue-Feng, Hua-Jun Zeng, and Zheng Chen. \"Finding group shilling in recommendation system.\" Special interest tracks and posters of the 14th international conference on World Wide Web. 2005. source
  • O'Donovan, John, and Barry Smyth. \"Is trust robust? An analysis of trust-based recommendation.\" Proceedings of the 11th international conference on Intelligent user interfaces. 2006. source
  • Hurley, Neil J., Michael P. O'Mahony, and Guenole CM Silvestre. \"Attacking recommender systems: A cost-benefit analysis.\" IEEE Intelligent Systems 22.3 (2007): 64-68. source
  • Patel, Krupa, et al. \"A state of art survey on shilling attack in collaborative filtering based recommendation system.\" Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 1. Springer, Cham, 2016. source
  • Fang, Minghong, et al. \"Poisoning attacks to graph-based recommender systems.\" Proceedings of the 34th Annual Computer Security Applications Conference. 2018. source
  • Hu, Rui, et al. \"Targeted poisoning attacks on social recommender systems.\" 2019 IEEE Global Communications Conference (GLOBECOM). IEEE, 2019. source
  • Zhang, Hengtong, et al. \"Practical data poisoning attack against next-item recommendation.\" Proceedings of The Web Conference 2020. 2020. source
  • Song, Junshuai, et al. \"Poisonrec: an adaptive data poisoning framework for attacking black-box recommender systems.\" 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 2020. source
  • Wu, Zih-Wun, Chiao-Ting Chen, and Szu-Hao Huang. \"Poisoning attacks against knowledge graph-based recommendation systems using deep reinforcement learning.\" Neural Computing and Applications (2021): 1-19. source
  • Chen, Liang, et al. \"Data poisoning attacks on neighborhood\u2010based recommender systems.\" Transactions on Emerging Telecommunications Technologies 32.6 (2021): e3872. source
  • Zhang, Hengtong, et al. \"Data Poisoning Attack against Recommender System Using Incomplete and Perturbed Data.\" Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2021. source
  • Fan, Wenqi, et al. \"Attacking Black-box Recommendations via Copying Cross-domain User Profiles.\" 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021. source
"},{"location":"research/technical/#diversity","title":"Diversity","text":"
  • Kexin Yin, Xiao Fang, Bintong Chen, Olivia R. Liu Sheng (2022) Diversity Preference-Aware Link Recommendation for Online Social Networks. Information Systems Research 0(0). source
"},{"location":"research/technical/#multi-sided","title":"Multi-Sided","text":"
  • Rastegari, Baharak, et al. \"Two-sided matching with partial information.\" Proceedings of the fourteenth ACM conference on Electronic Commerce. 2013. https://doi.org/10.1145/2482540.2482607
  • Malgonde, Onkar, et al. \"TAMING COMPLEXITY IN SEARCH MATCHING: TWO-SIDED RECOMMENDER SYSTEMS ON DIGITAL PLATFORMS.\" Mis Quarterly 44.1 (2020). https://aisel.aisnet.org/misq/vol44/iss1/5/
  • Malgonde, Onkar S., et al. \"Managing Digital Platforms with Robust Multi-Sided Recommender Systems.\" Journal of Management Information Systems 39.4 (2022): 938-968. https://doi.org/10.1080/07421222.2022.2127440
"},{"location":"research/technical/#followee-recommendation","title":"Followee Recommendation","text":"
  • Yaxuan Ran, Jiani Liu, Yishi Zhang (2023) Integrating Users\u2019 Contextual Engagements with Their General Preferences: An Interpretable Followee Recommendation Method. INFORMS Journal on Computing 0(0). https://doi.org/10.1287/ijoc.2023.1284
"},{"location":"research/technical/#reference-learning","title":"Reference Learning","text":"
  • Jiapeng Liu, Mi\u0142osz Kadzi\u0144ski, Xiuwu Liao (2023) Modeling Contingent Decision Behavior: A Bayesian Nonparametric Preference-Learning Approach. INFORMS Journal on Computing 0(0). https://doi.org/10.1287/ijoc.2023.1292
"},{"location":"research/technical/#operations-research","title":"Operations Research","text":""},{"location":"research/technical/#lagrangian-relaxation","title":"Lagrangian Relaxation","text":"
  • Fisher, Marshall L. \"The Lagrangian relaxation method for solving integer programming problems.\" Management science 27.1 (1981): 1-18. source
  • Ghoshal, Abhijeet, et al. \"Hiding Sensitive Information when Sharing Distributed Transactional Data.\" Information systems research 31.2 (2020): 473-490. source
"},{"location":"research/technical/#column-generation","title":"Column Generation","text":"
  • Menon, Syam, and Sumit Sarkar. \"Privacy and Big Data: Scalable Approaches to Sanitize Large Transactional Databases for Sharing.\" MIS Quarterly 40.4 (2016). source
  • Dash, Sanjeeb, Oktay G\u00fcnl\u00fck, and Dennis Wei. \"Boolean decision rules via column generation.\" arXiv preprint arXiv:1805.09901 (2018). source
"},{"location":"research/technical/#decision-under-uncertainty","title":"Decision Under Uncertainty","text":"
  • Sen, Suvrajeet, and Julia L. Higle. \"An introductory tutorial on stochastic linear programming models.\" Interfaces 29.2 (1999): 33-61. source
  • Alessio Trivella, Danial Mohseni-Taheri, Selvaprabu Nadarajah (2022) Meeting Corporate Renewable Power Targets. Management Science 0(0). source
"},{"location":"research/technical/#data-driven-optimization","title":"Data-Driven Optimization","text":"
  • Gah-Yi Ban, Cynthia Rudin (2018) The Big Data Newsvendor: Practical Insights from Machine Learning. Operations Research 67(1):90-108. source
  • L. Jeff Hong, Zhiyuan Huang, Henry Lam (2020) Learning-Based Robust Optimization: Procedures and Statistical Guarantees. Management Science 67(6):3447-3467. source
  • Dimitris Bertsimas, Nihal Koduri (2021) Data-Driven Optimization: A Reproducing Kernel Hilbert Space Approach. Operations Research 70(1):454-471. source
  • Keliang Wang, Leonardo Lozano, Carlos Cardonha, David Bergman (2023) Optimizing over an Ensemble of Trained Neural Networks. INFORMS Journal on Computing 0(0). https://doi.org/10.1287/ijoc.2023.1285
  • Omar Besbes, Omar Mouchtaki (2023) How Big Should Your Data Really Be? Data-Driven Newsvendor: Learning One Sample at a Time. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4725
"},{"location":"research/technical/#predict-then-optimize-paradigm","title":"Predict-then-Optimize Paradigm","text":"
  • Bertsimas, D., & Kallus, N. (2018). From Predictive to Prescriptive Analytics. ArXiv:1402.5481. source
  • Demirovic, E., Stuckey, P. J., Bailey, J., Chan, J., Leckie, C., Ramamohanarao, K., & Guns, T. (2019). Predict+Optimise with Ranking Objectives: Exhaustively Learning Linear Functions. 1078\u20131085.
  • Elmachtoub, A. N., & Grigas, P. (2020). Smart \u201cPredict, then Optimize.\u201d ArXiv:1710.08005. source
  • Mandi, J., Bucarey, V., Mulamba, M., & Guns, T. (2022). Predict and Optimize: Through the Lens of Learning to Rank. ArXiv:2112.03609. source
"},{"location":"research/technical/#multi-objective-optimization","title":"Multi-Objective Optimization","text":"
  • Arne Herzel, Stefan Ruzika, Clemens Thielen (2021) Approximation Methods for Multiobjective Optimization Problems: A Survey. INFORMS Journal on Computing 33(4):1284-1299. source
"},{"location":"research/technical/#e-commerce","title":"E-Commerce","text":"
  • Maximilian Schiffer, Nils Boysen, Patrick S. Klein, Gilbert Laporte, Marco Pavone (2022) Optimal Picking Policies in E-Commerce Warehouses. Management Science 0(0). source
  • Hanwei Li, David Simchi-Levi, Michelle Xiao Wu, Weiming Zhu (2022) Estimating and Exploiting the Impact of Photo Layout: A Structural Approach. Management Science 0(0). source
  • Goldstein, Anat; Oestreicher-Singer, Gal; Barzilay, Ohad; and Yahav, Inbal. 2022. \"Are We There Yet? Analyzing Progress in the Conversion Funnel Using the Diversity of Searched Products,\" MIS Quarterly, (46: 4) pp.2015-2054. source
"},{"location":"research/technical/#assortment-optimization","title":"Assortment Optimization","text":"
  • Zhen-Yu Chen, Zhi-Ping Fan, Minghe Sun (2022) Machine Learning Methods for Data-Driven Demand Estimation and Assortment Planning Considering Cross-Selling and Substitutions. INFORMS Journal on Computing 0(0). source
  • Santiago R. Balseiro, Antoine D\u00e9sir (2022) Incentive-Compatible Assortment Optimization for Sponsored Products. Management Science 0(0). source
  • Antoine D\u00e9sir, Vineet Goyal, Bo Jiang, Tian Xie, Jiawei Zhang (2023) Robust Assortment Optimization Under the Markov Chain Choice Model. Operations Research 0(0). https://doi.org/10.1287/opre.2022.2420
  • Ningyuan Chen, Andre A. Cire, Ming Hu, Saman Lagzi (2023) Model-Free Assortment Pricing with Transaction Data. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4651
"},{"location":"research/technical/#decision-analysis","title":"Decision Analysis","text":"
  • Eric Neyman, Tim Roughgarden (2023) From Proper Scoring Rules to Max-Min Optimal Forecast Aggregation. Operations Research 0(0). source
  • Ibrahim Abada, Xavier Lambin (2023) Artificial Intelligence: Can Seemingly Collusive Outcomes Be Avoided?. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4623
  • Asa B. Palley, Ville A. Satop\u00e4\u00e4 (2023) Boosting the Wisdom of Crowds Within a Single Judgment Problem: Weighted Averaging Based on Peer Predictions. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4648
"},{"location":"research/technical/#electric-vehicle","title":"Electric Vehicle","text":"
  • Wei Qi, Yuli Zhang, Ningwei Zhang (2023) Scaling Up Electric-Vehicle Battery Swapping Services in Cities: A Joint Location and Repairable-Inventory Model. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4731
"},{"location":"research/technical/#probabilistic-reasoning","title":"Probabilistic Reasoning","text":"
  • Li, Zhepeng, et al. \"Utility-based link recommendation for online social networks.\" Management Science 63.6 (2017): 1938-1952. source
  • Ghoshal, Abhijeet, Syam Menon, and Sumit Sarkar. \"Recommendations using information from multiple association rules: A probabilistic approach.\" Information Systems Research 26.3 (2015): 532-551. source
"},{"location":"research/technical/#agent-based-modeling-simulation","title":"Agent-based Modeling & Simulation","text":"
  • Bonabeau, Eric. \"Agent-based modeling: Methods and techniques for simulating human systems.\" Proceedings of the national academy of sciences 99.suppl 3 (2002): 7280-7287. source
  • Macal, Charles M., and Michael J. North. \"Tutorial on agent-based modeling and simulation.\" Proceedings of the Winter Simulation Conference, 2005.. IEEE, 2005. source
  • Railsback, Steven F., Steven L. Lytinen, and Stephen K. Jackson. \"Agent-based simulation platforms: Review and development recommendations.\" Simulation 82.9 (2006): 609-623. source
  • An, Li. \"Modeling human decisions in coupled human and natural systems: Review of agent-based models.\" Ecological Modelling 229 (2012): 25-36. source
  • Abar, Sameera, et al. \"Agent Based Modelling and Simulation tools: A review of the state-of-art software.\" Computer Science Review 24 (2017): 13-33. source
  • Mladenov, Martin, et al. \"RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems.\" arXiv preprint arXiv:2103.08057 (2021). source official webiste github
  • Dong, John Qi (2022) \"Using Simulation in Information Systems Research,\" Journal of the Association for Information Systems, 23(2), 408-417. source
  • Zhaolin Hu, L. Jeff Hong (2022) Robust Simulation with Likelihood-Ratio Constrained Input Uncertainty. INFORMS Journal on Computing 0(0). source
  • Lucy E. Morgan, Luke Rhodes-Leader, Russell R. Barton (2022) Reducing and Calibrating for Input Model Bias in Computer Simulation. INFORMS Journal on Computing 0(0). source
"},{"location":"research/technical/#video-content-structuring","title":"Video Content Structuring","text":"
  • Scholarpedia provides a Wikipedia page at Video Content Structuring - Scholarpedia
"},{"location":"research/technical/#team","title":"Team","text":"
  • Devine, Dennis J., and Jennifer L. Philips. \"Do smarter teams do better: A meta-analysis of cognitive ability and team performance.\" Small group research 32.5 (2001): 507-532. source
  • Kozlowski, Steve WJ, and Daniel R. Ilgen. \"Enhancing the effectiveness of work groups and teams.\" Psychological science in the public interest 7.3 (2006): 77-124. source
  • Wang, Xinyu, Zhou Zhao, and Wilfred Ng. \"A comparative study of team formation in social networks.\" International conference on database systems for advanced applications. Springer, Cham, 2015. source
  • Andrejczuk, Ewa, et al. \"The composition and formation of effective teams: computer science meets organizational psychology.\" The Knowledge Engineering Review 33 (2018). source
  • G\u00f3mez-Zar\u00e1, Diego, Leslie A. DeChurch, and Noshir S. Contractor. \"A taxonomy of team-assembly systems: Understanding how people use technologies to form teams.\" Proceedings of the ACM on Human-Computer Interaction 4.CSCW2 (2020): 1-36. source
  • Ju\u00e1rez, Julio, Cipriano Santos, and Carlos A. Brizuela. \"A Comprehensive Review and a Taxonomy Proposal of Team Formation Problems.\" ACM Computing Surveys (CSUR) 54.7 (2021): 1-33. source
"},{"location":"research/technical/#user-behavior","title":"User Behavior","text":""},{"location":"research/technical/#mobile","title":"Mobile","text":"
  • Shaohui Wu, Yong Tan, Yubo Chen, Yitian (Sky) Liang (2022) How Is Mobile User Behavior Different?\u2014A Hidden Markov Model of Cross-Mobile Application Usage Dynamics. Information Systems Research 0(0) source
"},{"location":"research/technical/#consumer-search","title":"Consumer Search","text":"
  • Raluca M. Ursu, Qianyun Zhang, Elisabeth Honka (2022) Search Gaps and Consumer Fatigue. Marketing Science 0(0). source
"},{"location":"research/technical/#behavior-change","title":"Behavior Change","text":"
  • Merz, M., & Steinherr, V. M. (2022). Process-based Guidance for Designing Behavior Change Support Systems: Marrying the Persuasive Systems Design Model to the Transtheoretical Model of Behavior Change. Communications of the Association for Information Systems, 50, pp-pp. source
"},{"location":"research/technical/#pricing","title":"Pricing","text":"
  • Jinzhi Bu, David Simchi-Levi, Li Wang (2022) Offline Pricing and Demand Learning with Censored Data. Management Science 0(0). source
  • Wen Chen, Ying He, Saurabh Bansal (2023) Customized Dynamic Pricing When Customers Develop a Habit or Satiation. Operations Research 0(0). https://pubsonline.informs.org/doi/abs/10.1287/opre.2022.2412
"},{"location":"research/technical/#dynamic-pricing","title":"Dynamic Pricing","text":"
  • N. Bora Keskin, Yuexing Li, Jing-Sheng Song (2022) Data-Driven Dynamic Pricing and Ordering with Perishable Inventory in a Changing Environment. Management Science 0(0). source
  • Jinzhi Bu, David Simchi-Levi, Yunzong Xu (2022) Online Pricing with Offline Data: Phase Transition and Inverse Square Law. Management Science 0(0). source
"},{"location":"research/technical/#auditing","title":"Auditing","text":"
  • Bouayad, Lina, Balaji Padmanabhan, and Kaushal Chari. \"Audit Policies Under the Sentinel Effect: Deterrence-Driven Algorithms.\" Information Systems Research 30.2 (2019): 466-485. source
"},{"location":"research/technical/#reliable-prediction","title":"Reliable Prediction","text":"
  • Romano, Yaniv, Evan Patterson, and Emmanuel Candes. \"Conformalized quantile regression.\" Advances in neural information processing systems 32 (2019). source code
  • Sesia, Matteo, and Emmanuel J. Cand\u00e8s. \"A comparison of some conformal quantile regression methods.\" Stat 9.1 (2020): e261. source
  • Model Agnostic Prediction Interval Estimator (MAPIE) is a python toolkit for prediction interval estimation.
  • Nam Ho-Nguyen, Fatma K\u0131l\u0131n\u00e7-Karzan (2022) Risk Guarantees for End-to-End Prediction and Optimization Processes. Management Science 0(0). source
"},{"location":"research/technical/#online-platforms","title":"Online Platforms","text":"
  • Nicole Immorlica, Brendan Lucier, Vahideh Manshadi, Alexander Wei (2022) Designing Approximately Optimal Search on Matching Platforms. Management Science 0(0). source
"},{"location":"research/technical/#advertising","title":"Advertising","text":"
  • Ranjit M. Christopher, Sungho Park, Sang Pil Han, Min-Kyu Kim (2022) Bypassing Performance Optimizers of Real Time Bidding Systems in Display Ad Valuation. Information Systems Research 0(0). source
  • Jessica Clark, Jean-Fran\u00e7ois Paiement, Foster Provost (2023) Who\u2019s Watching TV?. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1204
"},{"location":"research/technical/#artifact-generalization","title":"Artifact Generalization","text":"
  • Manoj A. Thomas , Yan Li , Allen S. Lee (2022) Generalizing the Information Systems Artifact. Information Systems Research 0(0). source
"},{"location":"research/technical/#healthcare_1","title":"Healthcare","text":"
  • John R. Birge, Ozan Candogan, Yiding Feng (2022) Controlling Epidemic Spread: Reducing Economic Losses with Targeted Closures. Management Science 0(0). source
  • Yu, Shuo; Chai, Yidong; Chen, Hsinchun; Sherman, Scott J.; and Brown, Randall A.. 2022. \"Wearable Sensor-Based Chronic Condition Severity Assessment: An Adversarial Attention-Based Deep Multisource Multitask Learning Approach,\" MIS Quarterly, (46: 3) pp.1355-1394. source
  • Wanyi Chen, Nilay Tanik Argon, Tommy Bohrmann, Benjamin Linthicum, Kenneth Lopiano, Abhishek Mehrotra, Debbie Travers, Serhan Ziya (2022) Using Hospital Admission Predictions at Triage for Improving Patient Length of Stay in Emergency Departments. Operations Research 0(0). source
  • Shuo Yu, Yidong Chai, Sagar Samtani, Hongyan Liu, Hsinchun Chen (2023) Motion Sensor\u2013Based Fall Prevention for Senior Care: A Hidden Markov Model with Generative Adversarial Network Approach. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1203
  • Matt Baucum, Anahita Khojandi, Rama Vasudevan, Ritesh Ramdhani (2023) Optimizing Patient-Specific Medication Regimen Policies Using Wearable Sensors in Parkinson\u2019s Disease. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4747
"},{"location":"research/technical/#security","title":"Security","text":"
  • Warut Khern-am-nuai, Matthew J. Hashim, Alain Pinsonneault, Weining Yang, Ninghui Li (2022) Augmenting Password Strength Meter Design Using the Elaboration Likelihood Model: Evidence from Randomized Experiments. Information Systems Research 0(0). source
"},{"location":"research/technical/#bot-detection","title":"Bot Detection","text":"
  • Victor Benjamin, T. S. Raghu (2022) Augmenting Social Bot Detection with Crowd-Generated Labels. Information Systems Research 0(0). source
"},{"location":"research/technical/#inventory-management","title":"Inventory Management","text":"
  • Meng Qi, Yuanyuan Shi, Yongzhi Qi, Chenxin Ma, Rong Yuan, Di Wu, Zuo-Jun (Max) Shen (2022) A Practical End-to-End Inventory Management Model with Deep Learning. Management Science 0(0). source
"},{"location":"research/technical/#auction","title":"Auction","text":"
  • Benedikt B\u00fcnz, Benjamin Lubin, Sven Seuken (2022) Designing Core-Selecting Payment Rules: A Computational Search Approach. Information Systems Research 33(4):1157-1173. source
"},{"location":"research/technical/#fraud-detection","title":"Fraud Detection","text":"
  • Weinmann, Markus; Valacich, Joseph; Schneider, Christoph; Jenkins, Jeffrey L.; and Hibbeln, Martin. 2022. \"The Path of the Righteous: Using Trace Data to Understand Fraud Decisions in Real Time (Open Access),\" MIS Quarterly, (46: 4) pp.2317-2336. source
"},{"location":"research/technical/#retail","title":"Retail","text":"
  • Junyu Cao, Wei Qi (2022) Stall Economy: The Value of Mobility in Retail on Wheels. Operations Research 0(0). source
"},{"location":"research/technical/#matching","title":"Matching","text":"
  • Yiding Feng, Rad Niazadeh, Amin Saberi (2023) Two-Stage Stochastic Matching and Pricing with Applications to Ride Hailing. Operations Research 0(0). https://doi.org/10.1287/opre.2022.2398
"},{"location":"research/technical/#response-prediction","title":"Response Prediction","text":"
  • Gang Chen, Shuaiyong Xiao, Chenghong Zhang, Huimin Zhao (2023) A Theory-Driven Deep Learning Method for Voice Chat\u2013Based Customer Response Prediction. Information Systems Research 0(0). https://doi.org/10.1287/isre.2022.1196
"},{"location":"research/technical/#risk-prediction","title":"Risk Prediction","text":"
  • Yang, Yi, et al. \"Unlocking the Power of Voice for Financial Risk Prediction: A Theory-Driven Deep Learning Design Approach.\" Management Information Systems Quarterly 47.1 (2023): 63-96. https://aisel.aisnet.org/misq/vol47/iss1/5
"},{"location":"research/technical/#online-reviews","title":"Online Reviews","text":"
  • Yu, Yifan, et al. \"Unifying Algorithmic and Theoretical Perspectives: Emotions in Online Reviews and Sales.\"Management Information Systems Quarterly 47.1 (2023): 127-160. https://aisel.aisnet.org/misq/vol47/iss1/7
  • Yang, Mingwen, et al. \"Responding to Online Reviews in Competitive Markets: A Controlled Diffusion Approach.\" Management Information Systems Quarterly 47.1 (2023): 161-194. https://aisel.aisnet.org/misq/vol47/iss1/8
"},{"location":"research/technical/#product-design","title":"Product Design","text":"
  • Alex Burnap, John R. Hauser, Artem Timoshenko (2023) Product Aesthetic Design: A Machine Learning Augmentation. Marketing Science 0(0). https://doi.org/10.1287/mksc.2022.1429
"},{"location":"research/technical/#license","title":"License","text":"

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Last Update On 2023-04-15.

"}]} \ No newline at end of file +{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"intro/","title":"Introduction","text":"

What to read as a Ph.D. student or researcher majoring in Management Information Systems (MIS)?

This reading list covers MIS-relevant journals, conferences, books, papers, and resources.

A considerable number of papers in the list comes from the MIS7420 Seminar in Management Information Systems.

Ordered alphabetically, Prof. Amit Mehra, Atanu Lahiri, Jianqing Chen, Srinivasan Raghunathan, Sumit Sarkar, Syam Menon, Vijay Mookerjee, Zhiqiang Zheng are in charge of this course and contribute a lot to this list.

Thanks to them for creating a diverse topic portfolio in this MIS reading list!

"},{"location":"intro/#what-is-mis","title":"What is MIS?","text":"
  • Monideepa Tarafdar, Guohou Shan, Jason Bennett Thatcher, Alok Gupta (2022) Intellectual Diversity in IS Research: Discipline-Based Conceptualization and an Illustration from Information Systems Research. Information Systems Research 33(4):1490-1510. source
IS Methods and Theories

Information systems researchers use a bevvy of research methods and theoretical lenses to explore phenomena of interest. The following links will take you to sites that have been developed by members of the IS community who are experts in particular areas.

Theories in IS Research

Design Research

Qualitative Research

Quantitative Research

Spatial Design Support Systems

Research Task Repository

Decision Support Systems

---- AIS - IS Research, Methods, and Theories

"},{"location":"intro/#about-the-author","title":"About The Author","text":"

Yihong Liu is a Ph.D. candidate in the Management Science, Information Systems Concentration at the Naveen Jindal School of Management, UT Dallas.

"},{"location":"intro/#disclaimer","title":"Disclaimer","text":"Click to expand and read the disclaimer.

Last updated: March 08, 2022

"},{"location":"intro/#interpretation-and-definitions","title":"Interpretation and Definitions","text":""},{"location":"intro/#interpretation","title":"Interpretation","text":"

The words of which the initial letter is capitalized have meanings defined under the following conditions.

The following definitions shall have the same meaning regardless of whether they appear in singular or in plural.

"},{"location":"intro/#definitions","title":"Definitions","text":"

For the purposes of this Disclaimer:

  • We (referred to as either \"We\", \"Us\" or \"Our\" in this Disclaimer) refers to the authors of \"MIS Reading List\".

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  • Website refers to MIS Reading List, accessible from https://liu-yihong.github.io/MISReadingList/

"},{"location":"intro/#disclaimer_1","title":"Disclaimer","text":"

The information contained on the Service is for general information purposes only.

We assume no responsibility for errors or omissions in the contents of the Service.

In no event shall we be liable for any special, direct, indirect, consequential, or incidental damages or any damages whatsoever, whether in an action of contract, negligence or other tort, arising out of or in connection with the use of the Service or the contents of the Service. We reserve the right to make additions, deletions, or modifications to the contents on the Service at any time without prior notice.

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Please note that we do not guarantee the accuracy, relevance, timeliness, or completeness of any information on these external websites.

"},{"location":"intro/#errors-and-omissions-disclaimer","title":"Errors and Omissions Disclaimer","text":"

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The Service may contain views and opinions which are those of the authors and do not necessarily reflect the official policy or position of any other author, agency, organization, employer or company, including us.

Comments published by users are their sole responsibility and the users will take full responsibility, liability and blame for any libel or litigation that results from something written in or as a direct result of something written in a comment. We are not liable for any comment published by users and reserves the right to delete any comment for any reason whatsoever.

"},{"location":"intro/#no-responsibility-disclaimer","title":"No Responsibility Disclaimer","text":"

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In no event shall we be liable for any special, incidental, indirect, or consequential damages whatsoever arising out of or in connection with your access or use or inability to access or use the Service.

"},{"location":"intro/#use-at-your-own-risk-disclaimer","title":"\"Use at Your Own Risk\" Disclaimer","text":"

All information in the Service is provided \"as is\", with no guarantee of completeness, accuracy, timeliness or of the results obtained from the use of this information, and without warranty of any kind, express or implied, including, but not limited to warranties of performance, merchantability and fitness for a particular purpose.

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"},{"location":"intro/#license","title":"License","text":"

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The emoji in the home page is designed by OpenMoji \u2013 the open-source emoji and icon project (License: CC BY-SA 4.0) and the favicon is designed by IconPark (License: Apache License 2.0).

"},{"location":"jobs/","title":"Jobs","text":"

During the months of July and August, schools will usually post openings for IS positions (note that the exact time frame may vary depending on the field, such as Marketing).

To be prepared for the hiring process, it is important to have all necessary documents ready, including your CV, teaching and research statement, teaching evaluations, recommendation letters, and any required paper copies (requirement lists may vary between schools). Keep in mind to only include projects and papers that you are well-versed in and able to clearly explain in a 2-minute summary in your CV.

You may also want to share your document package with your PhD colleagues.

"},{"location":"jobs/#is-academic-jobs-2023-2024","title":"IS Academic Jobs 2023-2024","text":"

You find new job position and want to share?

Feel free to submit the job information through this link!

Please note that jobs starting at August 2023 are excluded from this list.

"},{"location":"jobs/#where-to-find-is-jobs","title":"Where to Find IS Jobs","text":"

IS job openings can often be found at:

  1. Association for Information Systems (AIS) Career Services
  2. INFORMS Career Center
  3. INFORMS Connect - Available Positions Community (Login Required)
  4. Production and Operations Management Society (POMS) Placement List
  5. Interdisciplinoxy.com
  6. Akadeus.com
  7. FacultyVacancies.com
  8. AcademicJobsOnline
  9. IHE Careers
  10. AOM Career Services
  11. IS Jobs
"},{"location":"jobs/#how-is-jobs-pay","title":"How IS Jobs Pay","text":"

OpenPayrolls provides nationwide public salary database for federal agencies, states, counties, cities, universities, colleges, and K-12 schools.

"},{"location":"jobs/#states-limiting-tenure","title":"States Limiting Tenure","text":"
  1. highereddive, \"5 state (Texas, North Dakota, Louisiana, Florida, Iowa) plans to restrict faculty tenure you\u2019ll want to watch\"
  2. bestcolleges, \"Tenure Under Attack Nationwide(South Carolina, Georgia, Iowa)\"
  3. universityworldnews, \"Tenure is under attack in the US and your country is next\"
  4. thehill, \"Texas and Florida take steps to limit professor tenure at state schools\"
  5. ncnewsline, \"New bill targets tenure, calls for scrutiny of research at UNC System campuses, community colleges\"
  6. University of Tennessee, \"Periodic Post-Tenure Performance Reviews | Office of the Provost\"

Last Update On 2023-07-12.

"},{"location":"journals/","title":"MIS Journals","text":"Information Systems Research (ISR)

Information Systems Research (ISR) is an author-friendly peer-reviewed journal that publishes the best research in the information systems discipline. Its mission is to advance knowledge about the effective and efficient utilization of information technology by individuals, groups, organizations, society, and nations for the improvement of economic and social welfare.

The journal covers a wide variety of phenomena and topics related to the design, management, use, valuation, and impact of information technologies at different levels of analysis. ISR publishes research that examines topics from a wide range of research traditions including cognitive psychology, economics, computer science, operations research, design science, organization theory, organization behavior, sociology, and strategic management.

---- INFORMS - Information Systems Research / RSS Feed

Management Science (MS)

Management Science (MS) is a scholarly journal that publishes scientific research on the practice of management focusing on the problems, interest, and concerns of managers.

Within its scope are all aspects of management related to strategy, entrepreneurship, innovation, information technology, and organizations as well as all functional areas of business, such as accounting, finance, marketing, and operations.

---- INFORMS - Management Science / RSS Feed

MIS Quarterly (MISQ)

The MIS Quarterly\u2019s trifecta vision is to

(1) achieve impact on scholarship and practice as the leading source of novel and accreted IS knowledge,

(2) exhibit range in work published with respect to problem domains and stakeholders addressed as well as theoretical and methodological approaches used, and

(3) execute effective editorial processes in a timely manner.

---- MIS Quarterly / Unofficial RSS Feed

AIS - Senior Scholars' List of Premier Journals

The College of Senior Scholars encourages colleagues, as well as deans and department chairs, to treat a list of premier journals as the top journals in our field. Such a list is intended to provide more consistency and meaningfulness to tenure and promotion cases.

The journal list is limited to those in the \"IS field,\" and omits both multidisciplinary outlets and specialty areas. Nevertheless, the list recognizes topical, methodological, and geographical diversity. In addition, the review processes are stringent, editorial board members are widely-respected and recognized, and there is international readership and contribution.

The journals in the list are, in alphabetical order:

Decision Support Systems

European Journal of Information Systems

Information & Management

Information and Organization

Information Systems Journal

Information Systems Research

Journal of the AIS

Journal of Information Technology

Journal of MIS

Journal of Strategic Information Systems

MIS Quarterly

---- Senior Scholars' List of Premier Journals / Rankings of universities and authors based on the Senior Scholars' Basket of Journals

UTD24

The UT Dallas\u2019 Naveen Jindal School of Management has created a database to track publications in 24 leading business journals.

The database contains titles and author affiliations of papers published in these journals since 1990.

The information in the database is used to provide the top 100 business school rankings since 1990 based on the total contributions of faculty.

---- UTD24

FT50

The Financial Times conducted a review in May 2016 of the journals that count towards its research rank. As a result, the number of journals considered went up to 50 compared to 45 previously.

---- FT50 / archive

"},{"location":"journals/#other-journals","title":"Other Journals","text":""},{"location":"journals/#license","title":"License","text":"

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Last Update On 2023-03-03.

"},{"location":"references/","title":"Reference Books & Papers","text":""},{"location":"references/#the-academic-life","title":"The Academic Life","text":""},{"location":"references/#advice-tips","title":"Advice & Tips","text":"
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"},{"location":"references/#academic-writings","title":"Academic Writings","text":"
  • Cochrane, John H. \"Writing Tips for Ph. D. Students.\" Chicago, IL: University of Chicago, 2005. pdf Chinese Version
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  • Clark, Roy Peter. How to write short: Word craft for fast times. Little, Brown Spark, 2013. source
  • Bailey, Stephen. Academic writing: A handbook for international students. Routledge, 2014. source
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  • Silvia, Paul J. How to write a lot: A practical guide to productive academic writing. American Psychological Association, 2018. source
  • INFOGRAPHIC: The secret to using tenses in scientific writing source
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"},{"location":"references/#review-writings","title":"Review Writings","text":"
  • Elisabeth PainSep. 22, 2. (2016, September 22). \"How to review a paper\". Retrieved August 31, 2020. source archive
  • Rai, A. (2016). Editor's comments: writing a virtuous review. MIS Quarterly, 40(3), iii-x. pdf
  • Wiley. \"How to perform a peer review\". Retrieved August 31, 2020. source
  • Indiana University East. (2017). \"How to Write a Review of a Scholarly Article\". Retrieved August 31, 2020. source
  • Adams, J. (2020, August 6). \"How to Write an Article Review\". source
  • Ahmed, B. S. (2018, February 26). \"Tips and advice when you review a scientific paper\". Elsevier. source
  • MIS Quarterly. (2020). \"Reviewing for MIS Quarterly: Virtuous Reviewing at MIS Quarterly\". source.
"},{"location":"references/#response-to-reviews","title":"Response to Reviews","text":"
  • Pang, Min-Seok and Thatcher, Jason B. (2023) \"A Practical Guide for Successful Revisions and Engagements with Reviewers,\" Journal of the Association for Information Systems, 24(2), 317-327. source
"},{"location":"references/#academic-presentations","title":"Academic Presentations","text":"
  • Reinhart, Susan M. Giving academic presentations. Ann Arbor, MI: University of Michigan Press, 2002. source
  • Chivers, Barbara, and Michael Shoolbred. A Student\u2032 s Guide to Presentations: Making your Presentation Count. Sage, 2007. source
  • Graham Burton. Presenting: Deliver Academic Presentations with Confidence HarperCollins UK, 2014.
  • Rendle-Short, Johanna. The academic presentation: Situated talk in action. Routledge, 2016.
  • Nycyk, Michael. \"Academic and scientific poster presentation: a modern comprehensive guide.\" (2018): 1550-1552.
  • Guest, Michael. Conferencing and Presentation English for Young Academic. Springer, 2018. source
"},{"location":"references/#academic-career","title":"Academic Career","text":"
  • Showalter, English. The MLA guide to the job search: A handbook for departments and for PhDs and PhD candidates in English and foreign languages. Modern Language Assoc. of America, 1996.
  • Goldsmith, John A., John Komlos, and Penny Schine Gold. The Chicago guide to your academic career: A portable mentor for scholars from graduate school through tenure. University of Chicago Press, 2001.
  • Kelsky, Karen. The professor is in: The essential guide to turning your Ph. D. into a job. Crown, 2015. homepage
  • Vick, Julia Miller, Jennifer S. Furlong, and Rosanne Lurie. \"The academic job search handbook.\" The Academic Job Search Handbook. University of Pennsylvania Press, 2016.
  • Boice, Robert. Advice for new faculty members. Vol. 75. Needham Heights, MA: Allyn & Bacon, 2000.
  • Firth, David, Matt Germonprez, and Jason Thatcher. \"Managing your PhD student career: How to prepare for the job market.\" Communications of the Association for Information Systems 34.1 (2014): 5. source
  • Liu, Yihong. \"Notes for Ph.D. Job Interview Experiences 02\u201321-2020.\" Yihong Liu\u2019s Blog, 26 May 2020, source.
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  • Association for Information Systems (AIS) Career Services
  • INFORMS Career Center
  • Production and Operations Management Society (POMS) Placement List
  • Interdisciplinoxy.com
  • Akadeus.com
"},{"location":"references/#teaching","title":"Teaching","text":"
  • Bain, Ken. What the best college teachers do. Harvard University Press, 2004. source
  • Filene, Peter. The joy of teaching: A practical guide for new college instructors. Univ of North Carolina Press, 2009. source
  • Seldin, Peter, J. Elizabeth Miller, and Clement A. Seldin. The teaching portfolio: A practical guide to improved performance and promotion/tenure decisions. John Wiley & Sons, 2010. source
  • Colby, Anne, et al. Rethinking undergraduate business education: Liberal learning for the profession. John Wiley & Sons, 2011. source
  • Bowen, Jos\u00e9 Antonio. Teaching naked: How moving technology out of your college classroom will improve student learning. John Wiley & Sons, 2012.
  • Angelo, Thomas A., and K. Patricia Cross. Classroom Assessment Techniques: A Handbook for College Teachers. Jossey Bass Wiley, 2012. source
  • Lang, James M. Cheating Lessons: Learning from Academic Dishonesty. Harvard University Press, 2013. source
  • Doyle, Elaine, Patrick Buckley, and Conor Carroll, eds. Innovative business school teaching: Engaging the millennial generation. Routledge, 2014. source
  • David Gooblar, \"They Haven\u2019t Done the Reading. Again.\", 2014. archive
  • Carey, Benedict. How we learn: The surprising truth about when, where, and why it happens. Random House Trade Paperbacks, 2015.
  • Nilson, Linda B. Specifications grading: Restoring rigor, motivating students, and saving faculty time. Stylus Publishing, LLC, 2015. source
  • Henderson, Linda J. \"Start Talking: A Handbook for Engaging Difficult Dialogues in Higher Education.\" (2016): 56-60. source
  • Nilson, Linda B. Teaching at its best: A research-based resource for college instructors. John Wiley & Sons, 2016. source
  • Howard, Jay. \"Class Discussion: From Blank Stares to True Engagement.\", 2019. source archive
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  • M\u00fcller, S. D. (2022). Student Research as Legitimate Peripheral Participation. Communications of the Association for Information Systems, 50, pp-pp. source
  • Zheng, Lily. DEI Deconstructed: Your No-nonsense Guide to Doing the Work and Doing it Right. Berrett-Koehler Publishers, 2022. source
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  • Journal of Management Education
  • Management Teaching Review
  • Journal of Teaching in International Business
  • Journal of Education for Business
"},{"location":"references/#ai-education","title":"AI & Education","text":"

This list comes from the Center of Teaching and Learning at the University of Texas at Dallas along with other sources.

  • Julia Staffel, ChatGPT and Its Impact on Teaching Philosophy and Other Subjects video
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  • Derek Bruff, A Bigger, Badder Clippy: Enhancing Student Learning with AI Writing Tools source
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  • Gabby Jones / Bloomberg, ChatGPT Is a Wake-up Call to Revamp How We Teach Writing website
  • Joshua Wilson, Writing Without Thinking? There\u2019s a Place for ChatGPT \u2014 If Used Properly website
  • turnitin.com, AI-generated text: What educators are\u00a0saying source
  • turnitin.com, AI-generated text: An annotated hotlist for\u00a0educators source
  • turnitin.com, Guide for approaching AI-generated text in your\u00a0classroom source
  • SAN JOS\u00c9 STATE UNIVERSITY, Generative AI & ChatGPT: Resources for Instructors source
  • UCLA, ChatGPT and AI Resources source
  • Lance Eaton, Classroom Policies for AI Generative Tools source
"},{"location":"references/#ethics","title":"Ethics","text":"
  • Davison, Robert M., Maris G. Martinsons, and Louie HM Wong. \"The ethics of action research participation.\" Information Systems Journal (2021). source
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"},{"location":"references/#dress-code","title":"Dress Code","text":"
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"},{"location":"references/#statistics-and-probability","title":"Statistics and Probability","text":"
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"},{"location":"references/#optimization","title":"Optimization","text":"
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"},{"location":"references/#bayesian-optimization","title":"Bayesian Optimization","text":"
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"},{"location":"references/#econometrics","title":"Econometrics","text":"
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"},{"location":"references/#causal-inference","title":"Causal Inference","text":"
  • Pearl, Judea. \"Causal inference in statistics: An overview.\" Statistics surveys 3 (2009): 96-146. pdf
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  • Peters, Jonas, Dominik Janzing, and Bernhard Sch\u00f6lkopf. Elements of causal inference: foundations and learning algorithms. The MIT Press, 2017. source
  • Pearl, Judea, and Dana Mackenzie. The book of why: the new science of cause and effect. Basic books, 2018. author's website
  • Yao, Liuyi, et al. \"A survey on causal inference.\" arXiv preprint arXiv:2002.02770 (2020). source
  • Cunningham, Scott. \"Causal inference.\" Causal Inference. Yale University Press, 2021. author's website
  • Matheus Facure's handbook Causal Inference for The Brave and True github repository
  • Brady Neal's blog provides a good list of books
  • Brady Neal's blog also provides a good course \"Introduction to Causal Inference\"
"},{"location":"references/#game-theory","title":"Game Theory","text":"
  • Gibbons, Robert S. Game theory for applied economists. Princeton University Press, 1992.
  • Fudenberg, Drew, and Jean Tirole. Game theory. MIT press, 1991.
  • Myerson, Roger B. Game Theory: Analysis of Conflict. Harvard university press, 2013.
  • Osborne, Martin J., and Ariel Rubinstein. A course in game theory. MIT press, 1994.
"},{"location":"references/#industry-organization","title":"Industry Organization","text":"
  • Tirole, Jean. The theory of industrial organization. MIT press, 1988.
  • Vives, Xavier. Oligopoly pricing: old ideas and new tools. MIT press, 1999.
  • Martin, Stephen. Advanced industrial economics. Blackwell Publishers, 2002.
  • Belleflamme, Paul, and Martin Peitz. Industrial organization: markets and strategies. Cambridge University Press, 2015.
"},{"location":"references/#artificial-intelligence","title":"Artificial Intelligence","text":"
  • Shmueli, Galit. \"To explain or to predict?.\" Statistical science 25.3 (2010): 289-310. source
  • Shmueli, Galit, and Otto R. Koppius. \"Predictive analytics in information systems research.\" MIS quarterly (2011): 553-572. source
"},{"location":"references/#machine-learning","title":"Machine Learning","text":"
  • Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media, 2009. source
  • Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006. pdf
  • Barber, David. Bayesian reasoning and machine learning. Cambridge University Press, 2012. source
  • Murphy, Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.
  • Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. \"Deep learning.\" Cambridge: MIT press, 2016. source
  • Sergey Levine provides a course CS W182 / 282A - UC Berkeley at Designing, Visualizing and Understanding Deep Neural Networks
  • Murphy, Kevin P. Probabilistic machine learning: an introduction. MIT press, 2022. website
  • Murphy, Kevin P. Probabilistic machine learning: Advanced Topics. MIT press, 2023. website
  • Berente, Nicholas, et al. \"Managing artificial intelligence.\" MIS Quarterly 45.3 (2021): 1433-1450. source
  • Balaji Padmanabhan, Xiao Fang, Nachiketa Sahoo, and Andrew Burton-Jones. \"Machine Learning in Information Systems Research\", MIS Quarterly Editors' Comments, 2022. source
"},{"location":"references/#few-shot-learning","title":"Few-shot Learning","text":"
  • Wang, Yaqing, et al. \"Generalizing from a few examples: A survey on few-shot learning.\" ACM Computing Surveys (CSUR) 53.3 (2020): 1-34. source
"},{"location":"references/#transfer-learning","title":"Transfer Learning","text":"
  • Pan, Sinno Jialin, and Qiang Yang. \"A survey on transfer learning.\" IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359. source
  • Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. \"A survey of transfer learning.\" Journal of Big data 3.1 (2016): 1-40. source
  • Tan, Chuanqi, et al. \"A survey on deep transfer learning.\" International conference on artificial neural networks. Springer, Cham, 2018. source
  • Zhuang, Fuzhen, et al. \"A comprehensive survey on transfer learning.\" Proceedings of the IEEE 109.1 (2020): 43-76. source
"},{"location":"references/#reinforcement-learning","title":"Reinforcement Learning","text":"
  • Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. MIT press, 2018. pdf
  • Yang, Tianpei, et al. \"Exploration in deep reinforcement learning: A comprehensive survey.\" arXiv preprint arXiv:2109.06668 (2021). source
  • datawhalechina on github.com provides a course in Chinese at datawhalechina/easy-rl
  • Bolei Zhou on github.com provides a course in English at zhoubolei/introRL
  • DeepMind and UCL provide an introduction lecture on reinforcement learning at Reinforcement Learning Lecture Series 2021
  • Hao Dong, Zihan Ding and Shanghang Zhang offer an online version of the book \"Deep Reinforcement Learning: Fundamentals, Research and Applications\"
"},{"location":"references/#computer-vision","title":"Computer Vision","text":"
  • jbhuang0604 on github.com provides a curated list of computer vision resources at awesome-computer-vision
"},{"location":"references/#natural-language-processing","title":"Natural Language Processing","text":"
  • keon on github.com provides a curated list dedicated to natural language processing at awesome-nlp
"},{"location":"references/#stochastic-differential-equation","title":"Stochastic Differential Equation","text":"
  • Gardiner, Crispin W. \"Handbook of stochastic methods: for physics, chemistry and the natural sciences.\" (2004). source
  • Dixit, Robert K., and Robert S. Pindyck. \"Investment under uncertainty\". Princeton university press, 2012. source
  • Klebaner, Fima C. \"Introduction to stochastic calculus with applications\". World Scientific Publishing Company, 2012. source
  • Evans, Lawrence C. \"An introduction to stochastic differential equations\". Vol. 82. American Mathematical Soc., 2012. pdf
  • Mikosch, Thomas. \"Elementary stochastic calculus with finance in view\". World scientific, 1998. source
  • Oksendal, Bernt. \"Stochastic differential equations: an introduction with applications\". Springer Science & Business Media, 2013. source
  • Mao, Xuerong. \"Stochastic differential equations and applications\". Elsevier, 2007. source
"},{"location":"references/#theory-building","title":"Theory Building","text":"
  • Hassan, Nik Rushdi; Lowry, Paul Benjamin; and Mathiassen, Lars (2022) \"Useful Products in Information Systems Theorizing: A Discursive Formation Perspective,\" Journal of the Association for Information Systems, 23(2), 418-446. source
"},{"location":"references/#code-analysis","title":"Code Analysis","text":"
  • AnalysisTools
  • SonarQube
  • Codacy
  • deepsource
  • Semgrep
  • embold
  • Coverity Scan
"},{"location":"references/#license","title":"License","text":"

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Last Update On 2023-06-02.

"},{"location":"conferences/","title":"MIS Conferences","text":"The International Conference on Information Systems (ICIS)

The International Conference on Information Systems (ICIS) is the most prestigious gathering of information systems academics and research-oriented practitioners in the world. Every year its 270 or so papers and panel presentations are selected from more than 800 submissions. The conference activities are primarily delivered by and for academics, though many of the papers and panels have a strong professional orientation.

ICIS was founded in 1980 at UCLA and the first conference was held at the University of Pennsylvania as the \" Conference on Information Systems\". By 1986, particularly as the result of Canadian and European attendance and participation, \" International\" was appended to the name, thereby creating the International Conference on Information Systems. ICIS became truly international in 1990 when the conference was first held outside North America in Copenhagen, Denmark.

---- ICIS Home Page / ICIS Proceedings / 2023

Conference on Information Systems and Technology (CIST): 2022, 2023

Workshop on Information Systems and Economics (WISE)

Workshop on Information Technologies and Systems (WITS)

WITS, the Workshop on Information Technologies and Systems is an academic conference for information systems that is held annually in December in conjunction with ICIS (the International Conference on Information Systems).

The WITS community is focused on addressing complex business problems or societal issues using current and emerging information technologies.\u00a0 We also encourage research that can change the way information technology functions (e.g., by designing, modifying, or constructing systems) so that they can better solve real-world problems. All problem-solving paradigms \u2013 including empirical, analytical, behavioral, experimental, and computational \u2013 are invited. Integrative approaches, whether methodological or functional, are welcome.

WITS research is often\u00a0prescriptive\u00a0(toward providing a solution to a problem), rather than\u00a0descriptive\u00a0(explaining a phenomenon), unless the explanation clearly helps in developing a solution. We particularly invite work that is early, but has the potential to make a significant impact \u2013 innovation and novelty are at least as important as completeness and rigor.

---- WITS Home Page

Theory in Economics of Information Systems (TEIS)

The TEIS workshop is designed to provide a community for researchers who use analytical modeling techniques in the area of economics of information systems. Although a number of workshops and conferences accept research based on analytical models, these tend to be diffused with inadequate time for presentation, discussion and Q&A.

TEIS workshop complements such venues by providing a focused and intense environment for interaction among researchers to assist in the development of the field and help advance shared understanding about various aspects of modeling research. TEIS workshops have a single track with one hour per paper so everyone can participate substantively in the discussion.

---- TEIS Home Page

Statistical Challenges in Electronic Commerce Research (SCECR)

Started in 2005 by Ravi Bapna (currently at University of Minnesota), Wolfgang Jank (currently at University of South Florida), and Galit Schmueli (National Tsing Hua University), the Workshop on Statistical Conference in E-Commerce Research (SCECR) is a leading workshop attracting many top researchers throughout the world in the areas of information systems, quantitative marketing, economics,\u00a0 statistics, machine learning, and computer science.

The workshop covers diverse domains such as e-commerce, social media, digital content, finance, and telecommunications.\u00a0 Methods include econometric, machine learning, statistical inference, and unstructured and Big Data techniques.\u00a0 The theme this year will be related to Big Data and economic impact.

---- SCECR Home Page

Conference on Health IT and Analytics (CHITA)

The Conference on Health IT and Analytics (previously known as the Workshop on Health IT & Economics) is an annual health IT and analytics research summit, including a doctoral consortium that each year gathers prominent scholars from more than 40 research institutes, and leading policy and practitioner attendees in a vibrant setting to discuss opportunities and challenges in the design, implementation and management of health information technology and analytics.

Its goal is to deepen our understanding of strategy, policy and systems fostering health IT and analytics effective use and to stimulate new ideas with both policy and business implications. This forum provides a productive venue to facilitate collaboration among academia, government, and industry. Now in its eleventh year, each year CHITA draws over 120 participants.

---- 2022 / 2023

Hawaii International Conference on System Sciences (HICSS)

Since 1968, the Hawaii International Conference on System Sciences (HICSS) has been known worldwide as the longest-standing working scientific conferences in Information Technology Management.

HICSS provides a highly interactive working environment for top scholars from academia and the industry from over 60 countries to exchange ideas in various areas of information, computer, and system sciences. According to Microsoft Academic, HICSS ranks the 36th in terms of citations among 4,444 conferences in all fields worldwide.

---- HICSS Home Page

China Summer Workshop on Information Management (CSWIM)

As China has become a major player in the world\u2019s economy and various technological fields, information systems and management research opportunities are abundant for scholars around the globe.

The purpose of China Summer Workshop on Information Management (CSWIM) is to create a new bridge for promoting exchanges between scholars in China and overseas in the area of information systems and management. In particular, CSWIM focuses on creating a unique experience for MIS researchers around the world who would like to communicate and collaborate with China-based scholars.

---- CSWIM Home Page

China Workshop on Economics of Information Systems Theory (CWEIST)

The field of information systems has a long tradition of using analytical modeling (e.g. game-theoretical and mathematical models) to understand information systems phenomena and generate useful recommendations. With many emerging phenomena in IS, the need for this type of applied theory research is ever greater. However, the forums for this style of inquiry are rather limited, especially for analytical modeling scholars in China and the surrounding regions. The purpose of the China Workshop on Economics of Information Systems Theory (CWEIST) is to bring together a community of scholars in China and around the world with a shared interest in using analytical modeling to study issues in IS and related fields. We hope this summer workshop to become a unique forum for this community to exchange ideas, hone our skills, and form new collaborations across geographical boundaries.

---- CWEIST Home Page / 2023

Production and Operations Management Society (POMS) Conference

Production and Operations Management Society (POMS) is an international professional organization representing the interests of POM professionals from around the world.

The purposes of the Society are:

  • to extend and integrate knowledge that contributes to the improved understanding and practice of production and operations management (POM);
  • to disseminate information on POM to managers, scientists, educators, students, public and private organizations, national and local governments, and the general public; and
  • to promote the improvement of POM and its teaching in public and private manufacturing and service organizations throughout the world

---- POMS Home Page / POMS Conference Page / 2023

INFORMS Annual Meeting

The INFORMS Annual Meeting brings together over 6,000 people to the world's largest O.R. and analytics conference. Held each fall, the INFORMS Annual Meeting features more than 800 sessions and presentations, opportunities to meet with leading companies, universities and other exhibitors, an onsite career fair connecting top talent with over 100 organizations at the forefront of O.R. and analytics application, and other networking and educational events.

---- INFORMS Conference Home Page / 2023 INFORMS Annual Meeting

The Americas Conference on Information Systems (AMCIS)

The annual Americas Conference on Information Systems (AMCIS) is viewed as one of the leading conferences for presenting the broadest variety of research done by and for IS/IT academicians. Every year its papers and panel presentations are selected from over 700 submissions, and the AMCIS proceedings are in the permanent collections of libraries throughout the world.

---- AMCIS Home Page / AMCIS Proceedings

Pacific Asia\u00a0Conference on Information Systems (PACIS)

The annual Pacific Asia Conference on Information Systems (PACIS) is viewed as one of the leading conferences on information systems and the only AIS conference dedicated to the Pacific Asia Region. PACIS is endorsed by the AIS Council and governed by the AIS Region 3 Board.

---- PACIS Home Page / PACIS Proceedings

European Conference on Information Systems (ECIS)

The annual European Conference on Information Systems (ECIS) is viewed as one of the leading conferences on information systems and the only AIS conference\u00a0dedicated to\u00a0the European Region. ECIS is the newest regional conference endorsed by the AIS Council and governed by the AIS Region 2 Board.

---- ECIS Home Page / ECIS Proceedings

International Conference on Design Science Research in Information Systems and Technology (DESRIST)

Design\u00a0science research (DSR) in information\u00a0systems (IS) has received significant\u00a0attention in the information systems\u00a0research community. In an immersed\u00a0society, where there are numerous\u00a0wicked problems on all levels of analysis,\u00a0DSR is an ideal approach to understand\u00a0\u00a0complex challenges and support the\u00a0design of useful solutions, making\u00a0provision for rigour and relevance.\u00a0Based on multi-stakeholder problem\u00a0analysis and informed by existing\u00a0descriptive and design knowledge,\u00a0well-designed innovative methods,\u00a0solution patterns, reference models and\u00a0exemplary IS solutions promise to be\u00a0effective means of addressing many of\u00a0today\u2019s challenges \u2013 and will contribute\u00a0to the further development of DSR\u2019s\u00a0methodological foundations. The\u00a0better we get at integrating humans,\u00a0organisations and machines, the better\u00a0we will be able to use all means possible\u00a0to achieve the Sustainable Development\u00a0Goals (SDGs). The United Nations, with\u00a0its economic and social development\u00a0agenda, as it pertains to sustainability, ultimately impacts all countries,\u00a0organisations, teams and individuals through the SDGs.

---- 2023 / 2022 / 2021 / Springer Conference Proceedings List

IADIS Information Systems Conference

The IADIS Information Systems Conference aims to provide a forum for the discussion of IS taking a socio-technological perspective. It aims to address the issues related to design, development and use of IS in organisations from a socio-technological perspective, as well as to discuss IS professional practice, research and teaching.

---- 2023 / IADIS

International Conference on Information Systems Security and Privacy (ICISSP)

The International Conference on Information Systems Security and Privacy provides a meeting point for researchers and practitioners, addressing the trust, security and privacy challenges of information systems from both technological and social perspectives.

The conference welcomes papers of either practical or theoretical nature, and is interested in research or applications addressing all aspects of trust, security and privacy, and encompassing issue of concern for organizations, individuals and society at large.

---- ICISSP Home Page

INFORMS Conference Calendar

Conference Index - Information Systems Conferences

"},{"location":"conferences/#license","title":"License","text":"

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"},{"location":"research/analytical/","title":"Analytical Model","text":""},{"location":"research/analytical/#how-to-model","title":"How to Model","text":"
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"},{"location":"research/analytical/#adverse-selection-and-self-selection","title":"Adverse Selection and Self Selection","text":"
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  • Samir Mamadehussene (2023) Rebates Offered by a Multiproduct Firm. Marketing Science 0(0). https://doi.org/10.1287/mksc.2023.1430
"},{"location":"research/analytical/#two-sided-market","title":"Two-sided Market","text":"
  • Tunc, Murat M., Huseyin Cavusoglu, and Srinivasan Raghunathan. \"Two-Sided Adverse Selection and Bilateral Reviews in Sharing Economy.\" Available at SSRN 3499979 (2019). source
  • Yifan Dou, D. J. Wu (2021) Platform Competition Under Network Effects: Piggybacking and Optimal Subsidization. Information Systems Research 32(3):820-835. source
  • Manlu Chen, Ming Hu, Jianfu Wang (2022) Food Delivery Service and Restaurant: Friend or Foe?. Management Science 0(0). source
  • Saeed Alaei, Ali Makhdoumi, Azarakhsh Malekian, Sa\u0161a Peke\u010d (2022) Revenue-Sharing Allocation Strategies for Two-Sided Media Platforms: Pro-Rata vs. User-Centric. Management Science 0(0). source
  • Haurand, M. D. (2022). Looking Beyond Membership: A Simulation Study of Market-entry Strategies for Two-sided Platforms under Competition. Communications of the Association for Information Systems, 50, pp-pp. source
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"},{"location":"research/analytical/#externalities","title":"Externalities","text":"
  • August, Terrence, and Tunay I. Tunca. \"Network software security and user incentives.\" Management Science 52.11 (2006): 1703-1720. source
  • Koh, Byungwan, Srinivasan Raghunathan, and Barrie R. Nault. \"Is voluntary profiling welfare enhancing?.\" Management Information Systems Quarterly, Forthcoming (2015). source
"},{"location":"research/analytical/#platform","title":"Platform","text":""},{"location":"research/analytical/#online-platform","title":"Online Platform","text":"
  • Alexandre de Corni\u00e8re, Miklos Sarvary (2022) Social Media and News: Content Bundling and News Quality. Management Science 0(0). source
  • Yunke Mai, Bin Hu, Sa\u0161a Peke\u010d (2022) Courteous or Crude? Managing User Conduct to Improve On-Demand Service Platform Performance. Management Science 0(0). source
  • Pnina Feldman, Andrew E. Frazelle, Robert Swinney (2022) Managing Relationships Between Restaurants and Food Delivery Platforms: Conflict, Contracts, and Coordination. Management Science 0(0). source
"},{"location":"research/analytical/#user-generated-content","title":"User Generated Content","text":"
  • Dongwook Shin, Stefano Vaccari, Assaf Zeevi (2022) Dynamic Pricing with Online Reviews. Management Science 0(0). source
  • Pu, Jingchuan, et al. \"Platform policies and sellers\u2019 competition in agency selling in the presence of online quality misrepresentation.\" Journal of Management Information Systems 39.1 (2022): 159-186. source
  • Shin, Dongwook, and Assaf Zeevi. \"Product quality and information sharing in the presence of reviews.\" Management Science (2023). https://doi.org/10.1287/mnsc.2023.4746
"},{"location":"research/analytical/#platform-openness","title":"Platform Openness","text":"
  • Adner, Ron, Jianqing Chen, and Feng Zhu. \"Frenemies in platform markets: Heterogeneous profit foci as drivers of compatibility decisions.\" Management Science (2019). source
  • Chen, Jianqing, and Zhiling Guo. \"New media advertising and retail platform openness.\" source
"},{"location":"research/analytical/#versioning","title":"Versioning","text":"
  • Bhargava, Hemant K., and Vidyanand Choudhary. \"Information goods and vertical differentiation.\" Journal of Management Information Systems 18.2 (2001): 89-106. source
  • Lahiri, Atanu, and Debabrata Dey. \"Versioning and information dissemination: A new perspective.\" Information Systems Research 29.4 (2018): 965-983. source
"},{"location":"research/analytical/#contracting-and-moral-hazard","title":"Contracting and Moral Hazard","text":"
  • Cezar, Asunur, Huseyin Cavusoglu, and Srinivasan Raghunathan. \"Outsourcing information security: Contracting issues and security implications.\" Management Science 60.3 (2014): 638-657. source
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"},{"location":"research/analytical/#security","title":"Security","text":"
  • Dey, Debabrata, Atanu Lahiri, and Guoying Zhang. \"Hacker behavior, network effects, and the security software market.\" Journal of Management Information Systems 29.2 (2012): 77-108. source
  • Ghoshal, Abhijeet, Atanu Lahiri, and Debabrata Dey. \"Drawing a Line in the Sand: Commitment Problem in Ending Software Support.\" MIS Quarterly 41.4 (2017): 1227-1247. source
  • Terrence August, Duy Dao, Marius Florin Niculescu (2022) Economics of Ransomware: Risk Interdependence and Large-Scale Attacks. Management Science 0(0). source
"},{"location":"research/analytical/#privacy","title":"Privacy","text":"
  • T. Tony Ke, K. Sudhir (2022) Privacy Rights and Data Security: GDPR and Personal Data Markets. Management Science 0(0). source
  • Ashkan Eshghi, Ram D. Gopal, Hooman Hidaji, Raymond A. Patterson (2023) Now You See It, Now You Don\u2019t: Obfuscation of Online Third-Party Information Sharing. INFORMS Journal on Computing 0(0). https://doi.org/10.1287/ijoc.2022.1266
"},{"location":"research/analytical/#piracy","title":"Piracy","text":"
  • Lahiri, Atanu, and Debabrata Dey. \"Effects of piracy on quality of information goods.\" Management Science 59.1 (2013): 245-264. source
  • Kim, Antino, Atanu Lahiri, and Debabrata Dey. \"The\" Invisible Hand\" of Piracy: An Economic Analysis of the Information-Goods Supply Chain.\" MIS Quarterly 42.4 (2018). source
  • Chellappa, Ramnath K., and Shivendu Shivendu. \"Managing piracy: Pricing and sampling strategies for digital experience goods in vertically segmented markets.\" Information Systems Research 16.4 (2005): 400-417. source
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  • Jin, Chen, Chenguang Wu, and Atanu Lahiri. \"Piracy and Bundling of Information Goods.\" Journal of Management Information Systems 39.3 (2022): 906-933. source
  • Can Sun, Yonghua Ji, Xianjun Geng (2023) Which Enemy to Dance with? A New Role of Software Piracy in Influencing Antipiracy Strategies. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1219
"},{"location":"research/analytical/#online-advertising","title":"Online Advertising","text":"
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"},{"location":"research/analytical/#recommendation-personalization","title":"Recommendation & Personalization","text":"
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"},{"location":"research/analytical/#competition","title":"Competition","text":""},{"location":"research/analytical/#price-competition","title":"Price Competition","text":"
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"},{"location":"research/analytical/#sustainability","title":"Sustainability","text":"
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"},{"location":"research/analytical/#network","title":"Network","text":"

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"},{"location":"research/analytical/#information-sharing","title":"Information Sharing","text":"
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"},{"location":"research/analytical/#repeated-purchase","title":"Repeated Purchase","text":"
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"},{"location":"research/analytical/#license","title":"License","text":"

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Last Update On 2023-04-15.

"},{"location":"research/empirical/","title":"Empirical Model","text":""},{"location":"research/empirical/#empirical-methodology","title":"Empirical Methodology","text":"
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  • Ghose, Anindya, et al. \"Empowering patients using smart mobile health platforms: Evidence from a randomized field experiment.\" MIS Quarterly (2022). source
  • Clary, G., Dick, G., Akbulut, A. Y., & Van Slyke, C. (2022). The After Times: College Students\u2019 Desire to Continue with Distance Learning Post Pandemic. Communications of the Association for Information Systems, 50, pp-pp. source
  • Gorkem Turgut Ozer, Brad N. Greenwood, Anandasivam Gopal (2022) Digital Multisided Platforms and Women\u2019s Health: An Empirical Analysis of Peer-to-Peer Lending and Abortion Rates. Information Systems Research 0(0). source
  • Shelly Rathee, Kritika Narula, Arul Mishra, Himanshu Mishra (2022) Alphanumeric vs. Numeric Token Systems and the Healthcare Experience: Field Evidence from Healthcare Delivery in India. Management Science 0(0). source
  • Sykes, Tracy Ann, and Ruba Aljafari. \"We Are All in This Together, or Are We? Job Strain and Coping in the Context of an E-Healthcare System Implementation.\" Journal of Management Information Systems 39.4 (2022): 1215-1247. https://doi.org/10.1080/07421222.2022.2127450
  • Temidayo Adepoju, Anita L. Carson, Helen S. Jin, Christopher S. Manasseh (2023) Hospital Boarding Crises: The Impact of Urgent vs. Prevention Responses on Length of Stay. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4724
  • Sezgin Ayabakan, Indranil R. Bardhan, Zhiqiang (Eric) Zheng (2023) Impact of Telehealth and Process Virtualization on Healthcare Utilization. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1220
  • Thiebes, S., Gao, F., Briggs, R. O., Schmidt-Kraepelin, M., & Sunyaev, A. (2023). Design Concerns for Multiorganizational, Multistakeholder Collaboration: A Study in the Healthcare Industry. Journal of management information systems, 1. https://doi.org/10.1080/07421222.2023.2172771
"},{"location":"research/empirical/#pandemic","title":"Pandemic","text":"
  • Marta Serra-Garcia, Nora Szech (2022) Incentives and Defaults Can Increase COVID-19 Vaccine Intentions and Test Demand. Management Science 0(0). source
  • Joseph R. Buckman, Idris Adjerid, Catherine Tucker (2022) Privacy Regulation and Barriers to Public Health. Management Science 0(0). source
  • Jean-Philippe Bonardi, Quentin Gallea, Dimitrija Kalanoski, Rafael Lalive (2023) Managing Pandemics: How to Contain COVID-19 Through Internal and External Lockdowns and Their Release. Management Science 0(0). https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2022.4652
"},{"location":"research/empirical/#applications-of-artificial-intelligence","title":"Applications of Artificial Intelligence","text":"
  • Wang, Quan, Beibei Li, and Param Vir Singh. \"Copycats vs. original mobile apps: A machine learning copycat-detection method and empirical analysis.\" Information Systems Research 29.2 (2018): 273-291. source
  • Burtch, Gordon, Anindya Ghose, and Sunil Wattal. \"The hidden cost of accommodating crowdfunder privacy preferences: A randomized field experiment.\" Management Science 61.5 (2015): 949-962. source
  • V\u00edtor Albiero, and Kevin W. Bowyer. \"Is Face Recognition Sexist? No, Gendered Hairstyles and Biology Are\" BMVC 2020. source
  • Garvey, Aaron M., et al. \u201cBad News? Send an AI. Good News? Send a Human.\u201d Journal of Marketing, Feb. 2022. source
  • Martin Reisenbichler, Thomas Reutterer, David A. Schweidel, Daniel Dan (2022) Frontiers: Supporting Content Marketing with Natural Language Generation. Marketing Science 0(0). source
  • Andreas Barth, Sasan Mansouri, Fabian W\u00f6bbeking, (2022) \u201cLet Me Get Back to You\u201d\u2014A Machine Learning Approach to Measuring NonAnswers. Management Science 0(0). source
"},{"location":"research/empirical/#software","title":"Software","text":""},{"location":"research/empirical/#piracy","title":"Piracy","text":"
  • Martin Eisend. \"Explaining Digital Piracy: A Meta-Analysis.\" Information Systems Research 30.2 (2019): 636-664. source
  • Christian Peukert, Stefan Bechtold, Michail Batikas, Tobias Kretschmer (2022) Regulatory Spillovers and Data Governance: Evidence from the GDPR. Marketing Science 0(0). source
"},{"location":"research/empirical/#cybersecurity","title":"Cybersecurity","text":"
  • Kolini, F., & Janczewski, L. J. (2022). Exploring Incentives and Challenges for Cybersecurity Intelligence Sharing (CIS) across Organizations: A Systematic Review. Communications of the Association for Information Systems, 50, pp-pp. source
  • D'Arcy, John and Basoglu, Asli (2022) \"The Influences of Public and Institutional Pressure on Firms\u2019 Cybersecurity Disclosures,\" Journal of the Association for Information Systems, 23(3), 779-805. source
  • A. J. Burns, Tom L. Roberts, Clay Posey, Paul Benjamin Lowry, Bryan Fuller (2022) Going Beyond Deterrence: A Middle-Range Theory of Motives and Controls for Insider Computer Abuse. Information Systems Research 0(0). source
  • Nikkhah, Hamid Reza and Grover, Varun. 2022. \"An Empirical Investigation of Company Response to Data Breaches,\" MIS Quarterly, (46: 4) pp.2163-2196. source
"},{"location":"research/empirical/#electronic-participation","title":"Electronic Participation","text":"
  • Yo, Y., & Xu, P. (2022). The Power of Electronic Channels and Electronic Political Efficacy: Electronic Participation Discourse. Communications of the Association for Information Systems, 50, pp-pp. source
"},{"location":"research/empirical/#productivity","title":"Productivity","text":"
  • Peng Huang, Marco Ceccagnoli, Chris Forman, D.J. Wu (2022) IT Knowledge Spillovers, Absorptive Capacity, and Productivity: Evidence from Enterprise Software. Information Systems Research 0(0). source
"},{"location":"research/empirical/#software-development","title":"Software Development","text":"
  • Gregory Vial (2023) A Complex Adaptive Systems Perspective of Software Reuse in the Digital Age: An Agenda for IS Research. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1200
"},{"location":"research/empirical/#algorithm","title":"Algorithm","text":""},{"location":"research/empirical/#impact","title":"Impact","text":"
  • Athey, Susan. \"The impact of machine learning on economics.\" The economics of artificial intelligence: An agenda. University of Chicago Press, 2018. 507-547. pdf
"},{"location":"research/empirical/#bias","title":"Bias","text":"
  • Lambrecht, Anja, and Catherine Tucker. \"Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads.\" Management Science 65.7 (2019): 2966-2981. source
"},{"location":"research/empirical/#aversion","title":"Aversion","text":"
  • Dietvorst, Berkeley J., Joseph P. Simmons, and Cade Massey. \"Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them.\" Management Science 64.3 (2018): 1155-1170. source
  • Germann, Maximilian, and Christoph Merkle. \"Algorithm Aversion in Financial Investing.\" Available at SSRN 3364850 (2019). source
"},{"location":"research/empirical/#human-algorithm","title":"Human & Algorithm","text":"
  • Kleinberg, Jon, et al. \"Human decisions and machine predictions.\" The quarterly journal of economics 133.1 (2018): 237-293. source
  • Liwei Chen, J. J. Po-An Hsieh, Arun Rai (2022) How Does Intelligent System Knowledge Empowerment Yield Payoffs? Uncovering the Adaptation Mechanisms and Contingency Role of Work Experience. Information Systems Research 0(0). source
  • Tarafdar, Monideepa, Xinru Page, and Marco Marabelli. \"Algorithms as co\u2010workers: Human algorithm role interactions in algorithmic work.\" Information Systems Journal. source
  • Chen, Yang, et al. \"Does Techno-invasion Lead to Employees\u2019 Deviant Behaviors?.\" Journal of Management Information Systems 39.2 (2022): 454-482. source
  • You, Sangseok, Cathy Liu Yang, and Xitong Li. \"Algorithmic versus Human Advice: Does Presenting Prediction Performance Matter for Algorithm Appreciation?.\" Journal of Management Information Systems 39.2 (2022): 336-365. source
  • Ghasemaghaei, Maryam, and Ofir Turel. \"Why Do Data Analysts Take IT-Mediated Shortcuts? An Ego-Depletion Perspective.\" Journal of Management Information Systems 39.2 (2022): 483-512. source
  • Elizabeth Han, Dezhi Yin, Han Zhang (2022) Bots with Feelings: Should AI Agents Express Positive Emotion in Customer Service?. Information Systems Research 0(0). source
  • Mikhail Lysyakov , Siva Viswanathan (2022) Threatened by AI: Analyzing Users\u2019 Responses to the Introduction of AI in a Crowd-Sourcing Platform. Information Systems Research 0(0). source
  • Nasim Mousavi, Panagiotis Adamopoulos, Jesse Bockstedt (2023) The Decoy Effect and Recommendation Systems. Information Systems Research 0(0). https://doi.org/10.1287/isre.2022.1197
  • Callen Anthony, Beth A. Bechky, Anne-Laure Fayard (2023) \u201cCollaborating\u201d with AI: Taking a System View to Explore the Future of Work. Organization Science 0(0). https://doi.org/10.1287/orsc.2022.1651
  • Chandra, Shalini, Anuragini Shirish, and Shirish C. Srivastava. \"To Be or Not to Be\u2026 Human? Theorizing the Role of Human-Like Competencies in Conversational Artificial Intelligence Agents.\" Journal of Management Information Systems 39.4 (2022): 969-1005. https://doi.org/10.1080/07421222.2022.2127441
  • Tarafdar, Monideepa, Xinru Page, and Marco Marabelli. \"Algorithms as co\u2010workers: Human algorithm role interactions in algorithmic work.\" Information Systems Journal (2022). https://doi.org/10.1111/isj.12389
  • Kevin Bauer, Moritz von Zahn, Oliver Hinz (2023) Expl(AI)ned: The Impact of Explainable Artificial Intelligence on Users\u2019 Information Processing. Information Systems Research 0(0). https://doi.org/10.1287/isre.2023.1199
  • Parker, Sara, and Derek Ruths. \"Is hate speech detection the solution the world wants?.\" Proceedings of the National Academy of Sciences 120.10 (2023): e2209384120. https://doi.org/10.1073/pnas.2209384120
  • Chandra, Shalini, Anuragini Shirish, and Shirish C. Srivastava. \"To Be or Not to Be\u2026 Human? Theorizing the Role of Human-Like Competencies in Conversational Artificial Intelligence Agents.\" Journal of Management Information Systems 39.4 (2022): 969-1005. https://doi.org/10.1080/07421222.2022.2127441
  • Boyac\u0131, Tamer, Caner Canyakmaz, and Francis de V\u00e9ricourt. \"Human and Machine: The Impact of Machine Input on Decision Making Under Cognitive Limitations.\" Management Science (2023). https://doi.org/10.1287/mnsc.2023.4744
  • Dolata, M., Katsiuba, D., Wellnhammer, N., & Schwabe, G. (2023). Learning with Digital Agents: An Analysis based on the Activity Theory. Journal of Management Information Systems, 40(1), 56-95. https://doi.org/10.1080/07421222.2023.2172775
"},{"location":"research/empirical/#gender","title":"Gender","text":"
  • Lin, Chen, et al. \"Do \"Little Emperors\u201d Get More Than \u201cLittle Empresses\"? Boy-Girl Gender Discrimination as Evidenced by Consumption Behavior of Chinese Households.\" Marketing Science (2021). source
  • Helena Fornwagner, Monika Pompeo, Nina Serdarevic (2022) Choosing Competition on Behalf of Someone Else. Management Science 0(0). source
  • Emilio J. Castilla, Hye Jin Rho (2023) The Gendering of Job Postings in the Online Recruitment Process. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4674
  • Eliot L. Sherman, Raina Brands, Gillian Ku (2023) Dropping Anchor: A Field Experiment Assessing a Salary History Ban with Archival Replication. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4658
  • Zhiyan Wu, Lucia Naldi, Karl Wennberg, Timur Uman (2023) Learning from Their Daughters: Family Exposure to Gender Disparity and Female Representation in Male-Led Ventures. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4727
"},{"location":"research/empirical/#privacy","title":"Privacy","text":"
  • Godinho de Matos, Miguel, and Idris Adjerid. \"Consumer consent and firm targeting after GDPR: The case of a large telecom provider.\" Management Science (2021). source
  • Heng Xu, Nan Zhang (2022) From Contextualizing to Context Theorizing: Assessing Context Effects in Privacy Research. Management Science 0(0). source
  • Karwatzki, Sabrina et al. The multidimensional nature of privacy risks: Conceptualisation, measurement and implications for digital services. Information Systems Journal (2022). source
  • Tesary Lin (2022) Valuing Intrinsic and Instrumental Preferences for Privacy. Marketing Science 0(0). source
  • Tawfiq Alashoor, Mark Keil, H. Jeff Smith, Allen R. McConnell (2022) Too Tired and in Too Good of a Mood to Worry About Privacy: Explaining the Privacy Paradox Through the Lens of Effort Level in Information Processing. Information Systems Research 0(0). source
  • Ram D. Gopal, Hooman Hidaji, Sule Nur Kutlu, Raymond A. Patterson, Niam Yaraghi (2023) Law, Economics, and Privacy: Implications of Government Policies on Website and Third-Party Information Sharing. Information Systems Research 0(0). https://doi.org/10.1287/isre.2022.1178
  • Garrett A. Johnson, Scott K. Shriver, Samuel G. Goldberg (2023) Privacy and Market Concentration: Intended and Unintended Consequences of the GDPR. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4709
"},{"location":"research/empirical/#online-platforms","title":"Online Platforms","text":""},{"location":"research/empirical/#subscription-models","title":"Subscription Models","text":"
  • Oestreicher-Singer, Gal, and Lior Zalmanson. \"Content or community? A digital business strategy for content providers in the social age.\" MIS quarterly (2013): 591-616. source
  • Bapna, Ravi, and Akhmed Umyarov. \"Do your online friends make you pay? A randomized field experiment on peer influence in online social networks.\" Management Science 61.8 (2015): 1902-1920. source
  • Hongfei Li, Jing Peng, Xinxin Li, Jan Stallaert (2022) When More Can Be Less: The Effect of Add-On Insurance on the Consumption of Professional Services. Information Systems Research 0(0). source
"},{"location":"research/empirical/#digital-content-user-generated-content","title":"Digital Content & User-Generated Content","text":"
  • Ye, Hua, et al. \"Monetization of Digital Content: Drivers of Revenue on Q&A Platforms.\" Journal of Management Information Systems 38.2 (2021): 457-483. source
  • Zhiyu Zeng, Hengchen Dai, Dennis J. Zhang, Heng Zhang, Renyu Zhang, Zhiwei Xu, Zuo-Jun Max Shen (2022) The Impact of Social Nudges on User-Generated Content for Social Network Platforms. Management Science 0(0). source
  • Lu, S., Dinner, I., & Grewal, R. (2023). The Ripple Effect of Firm-Generated Content on New Movie Releases. Journal of Marketing Research, 0(0). https://doi.org/10.1177/00222437221143066
"},{"location":"research/empirical/#online-reviews","title":"Online Reviews","text":"
  • Chen, Yan, et al. \"Social comparisons and contributions to online communities: A field experiment on movielens.\" American Economic Review 100.4 (2010): 1358-98. source
  • Gordon Burtch, Yili Hong, Ravi Bapna, Vladas Griskevicius (2017) Stimulating Online Reviews by Combining Financial Incentives and Social Norms. Management Science 64(5):2065-2082. source
  • Limin Fang (2022) The Effects of Online Review Platforms on Restaurant Revenue, Consumer Learning, and Welfare. Management Science 0(0). source
  • Jinghui (Jove) Hou, Xiao Ma (2022) Space Norms for Constructing Quality Reviews on Online Consumer Review Sites. Information Systems Research 0(0). source
  • Sherry He, Brett Hollenbeck, Davide Proserpio (2022) The Market for Fake Reviews. Marketing Science 0(0). source
  • Honglin Deng, Weiquan Wang, Siyuan Li, and Kai H. Lim. \"Can Positive Online Social Cues Always Reduce User Avoidance of Sponsored Search Results?.\" MIS Quarterly (2021). source
  • Mengxia Zhang, Lan Luo (2022) Can Consumer-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp. Management Science 0(0). source
  • Choi, HanByeol Stella, et al. \"Effects of Online Crowds on Self-Disclosure Behaviors in Online Reviews: A Multidimensional Examination.\" Journal of Management Information Systems 39.1 (2022): 218-246. source
  • T. Ravichandran, Chaoqun Deng (2022) Effects of Managerial Response to Negative Reviews on Future Review Valence and Complaints. Information Systems Research 0(0). source
  • Jung, M., Ryu, S., Han, S. P., & Cho, D. (2023). Ask for Reviews at the Right Time: Evidence from Two Field Experiments. Journal of Marketing, 0(0). https://doi.org/10.1177/00222429221143329
  • Chen, Y., & Lee, S. (2023). User-Generated Physician Ratings and Their Effects on Patients\u2019 Physician Choices: Evidence from Yelp. Journal of Marketing, 0(0). https://doi.org/10.1177/00222429221146511
  • Uttara Ananthakrishnan, Davide Proserpio, Siddhartha Sharma (2023) I Hear You: Does Quality Improve with Customer Voice?. Marketing Science 0(0). https://doi.org/10.1287/mksc.2023.1437
  • Andrey Fradkin, David Holtz (2023) Do Incentives to Review Help the Market? Evidence from a Field Experiment on Airbnb. Marketing Science 0(0). https://doi.org/10.1287/mksc.2023.1439
"},{"location":"research/empirical/#digital-beauty-filter","title":"Digital Beauty Filter","text":"
  • I asked an AI to tell me how beautiful I am
  • Beauty filters are changing the way young girls see themselves
  • TikTok changed the shape of some people\u2019s faces without asking
  • How digital beauty filters perpetuate colorism
  • Losh, Elizabeth. \"Selfies| Feminism Reads Big Data:\" Social Physics,\" Atomism, and Selfiecity.\" International Journal of Communication 9 (2015): 13. source
  • Elias, Ana Sofia, and Rosalind Gill. \"Beauty surveillance: The digital self-monitoring cultures of neoliberalism.\" European Journal of Cultural Studies 21.1 (2018): 59-77. source
"},{"location":"research/empirical/#platform-growth-merge-and-acquisition","title":"Platform Growth, Merge and Acquisition","text":"
  • Chiara Farronato, Jessica Fong, Andrey Fradkin (2023) Dog Eat Dog: Balancing Network Effects and Differentiation in a Digital Platform Merger. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4675
"},{"location":"research/empirical/#social-media","title":"Social Media","text":"
  • Xu, Haifeng, et al (2022) \"Why Are People Addicted to SNS? Understanding the Role of SNS Characteristics in the Formation of SNS Addiction,\" Journal of the Association for Information Systems, 23(3), 806-837. source
  • Peng, Jing, Juheng Zhang, and Ram Gopal. \"The Good, the Bad, and the Social Media: Financial Implications of Social Media Reactions to Firm-Related News.\" Journal of Management Information Systems 39.3 (2022): 706-732. source
"},{"location":"research/empirical/#interactions","title":"Interactions","text":"
  • Matook, Sabine, Alan R. Dennis, and Yazhu Maggie Wang. \"User comments in social media firestorms: A mixed-method study of purpose, tone, and motivation.\" Journal of Management Information Systems 39.3 (2022): 673-705. source
  • Lu, Yingda; Wu, Junjie; Tan, Yong; and Chen, Jian. 2022. \"Microblogging Replies and Opinion Polarization: A Natural Experiment,\" MIS Quarterly, (46: 4) pp.1901-1936. source
  • Yun Young Hur, Fujie Jin, Xitong Li, Yuan Cheng, Yu Jeffrey Hu (2022) Does Social Influence Change with Other Information Sources? A Large-Scale Randomized Experiment in Medical Crowdfunding. Information Systems Research 0(0). source
  • Wakefield, R. L., & Wakefield, K. (2022). The antecedents and consequences of intergroup affective polarisation on social media. Information Systems Journal, 1\u2013 29. source
  • Miller, Stacy, et al. \"Integrating truth bias and elaboration likelihood to understand how political polarisation impacts disinformation engagement on social media.\" Information Systems Journal (2022). source
  • Wang, Lin, Chong Wang, and Xinyan Yao. \"Befriended to polarise? The impact of friend identity on review polarisation\u2014A quasi\u2010experiment.\" Information Systems Journal. https://doi.org/10.1111/isj.12425
"},{"location":"research/empirical/#fake-news-on-social-media","title":"Fake News on Social Media","text":"
  • Wang, Shuting, Min-Seok Pang, and Paul A. Pavlou. \"Cure or Poison? Identity Verification and the Posting of Fake News on Social Media.\" Journal of Management Information Systems 38.4 (2021): 1011-1038. source
  • Horner, Christy Galletta, et al. \"Emotions: The Unexplored Fuel of Fake News on Social Media.\" Journal of Management Information Systems 38.4 (2021): 1039-1066. source
  • Deng, Bingjie, and Michael Chau. \"The Effect of the Expressed Anger and Sadness on Online News Believability.\" Journal of Management Information Systems 38.4 (2021): 959-988. source
  • Turel, Ofir, and Babajide Osatuyi. \"Biased Credibility and Sharing of Fake News on Social Media: Considering Peer Context and Self-Objectivity State.\" Journal of Management Information Systems 38.4 (2021): 931-958. source
  • Ng, Ka Chung, Jie Tang, and Dongwon Lee. \"The Effect of Platform Intervention Policies on Fake News Dissemination and Survival: An Empirical Examination.\" Journal of Management Information Systems 38.4 (2021): 898-930. source
  • George, Jordana, Natalie Gerhart, and Russell Torres. \"Uncovering the Truth about Fake News: A Research Model Grounded in Multi-Disciplinary Literature.\" Journal of Management Information Systems 38.4 (2021): 1067-1094. source
  • Gimpel, Henner, et al. \"The effectiveness of social norms in fighting fake news on social media.\" Journal of Management Information Systems 38.1 (2021): 196-221. source
  • Mohamed Mostagir, Asuman Ozdaglar, James Siderius (2022) When Is Society Susceptible to Manipulation?. Management Science 0(0). source
  • Jackie London Jr., Siyuan Li, Heshan Sun (2022) Seems Legit: An Investigation of the Assessing and Sharing of Unverifiable Messages on Online Social Networks. Information Systems Research 0(0). source
  • Mohamed Mostagir, James Siderius (2022) Learning in a Post-Truth World. Management Science 0(0). source
  • Wang, Shuting (Ada); Pang, Min-Seok; and Pavlou, Paul A.. 2022. \"Seeing Is Believing? How Including a Video in Fake News Influences Users\u2019 Reporting of Fake News to Social Media Platforms,\" MIS Quarterly, (46: 3) pp.1323-1354. source
  • Gizem Ceylan, Ian A. Anderson, and Wendy Wood. 2022. \"Sharing of misinformation is habitual, not just lazy or biased,\" PNAS, (120:4) https://doi.org/10.1073/pnas.2216614120
"},{"location":"research/empirical/#social-media-marketing","title":"Social Media Marketing","text":"
  • Tingting Nian, Arun Sundararajan (2022) Social Media Marketing, Quality Signaling, and the Goldilocks Principle. Information Systems Research 0(0). source
  • Jens Foerderer, Sebastian W. Schuetz (2022) Data Breach Announcements and Stock Market Reactions: A Matter of Timing?. Management Science 0(0). source
  • Naveen Kumar, Liangfei Qiu, Subodha Kumar (2022) A Hashtag Is Worth a Thousand Words: An Empirical Investigation of Social Media Strategies in Trademarking Hashtags. Information Systems Research 0(0). source
  • Venkatesan, Srikanth, et al. \"INFLUENCE IN SOCIAL MEDIA: AN INVESTIGATION OF TWEETS SPANNING THE 2011 EGYPTIAN REVOLUTION.\" MIS Quarterly 45.4 (2021). source
  • Alibakhshi, Reza, and Shirish C. Srivastava. \"Post-Story: Influence of Introducing Story Feature on Social Media Posts.\" Journal of Management Information Systems 39.2 (2022): 573-601. source
  • Weiler, Michael, et al. \" Social Capital Accumulation Through Social Media Networks: Evidence from a Randomized Field Experiment and Individual-Level Panel Data,\" Management Information Systems Quarterly, (2021). source
  • Leung, Fine F., et al. \"Influencer Marketing Effectiveness.\" Journal of Marketing (2022) source
  • Liadeli, G., Sotgiu, F., & Verlegh, P. W. J. (2022). A Meta-Analysis of the Effects of Brands\u2019 Owned Social Media on Social Media Engagement and Sales. Journal of Marketing, 0(0). source
  • Woolley, K., Kupor, D., & Liu, P. J. (2022). Does Company Size Shape Product Quality Inferences? Larger Companies Make Better High-Tech Products, but Smaller Companies Make Better Low-Tech Products. Journal of Marketing Research, 0(0). source
"},{"location":"research/empirical/#norms-and-roles","title":"Norms And Roles","text":"
  • Emmanuelle Vaast, Alain Pinsonneault (2022) Dealing with the Social Media Polycontextuality of Work. Information Systems Research 0(0). source
  • Verena Schoenmueller, Oded Netzer, Florian Stahl (2022) Frontiers: Polarized America: From Political Polarization to Preference Polarization. Marketing Science 0(0). source
"},{"location":"research/empirical/#social-investing","title":"Social Investing","text":"
  • Jake An, Donnel Briley, Shai Danziger, Shai Levi (2022) The Impact of Social Investing on Charitable Donations. Management Science 0(0). source
"},{"location":"research/empirical/#network-graph","title":"Network & Graph","text":"
  • Mariia Petryk, Michael Rivera, Siddharth Bhattacharya, Liangfei Qiu, Subodha Kumar (2022) How Network Embeddedness Affects Real-Time Performance Feedback: An Empirical Investigation. Information Systems Research 0(0). source
  • Rohit Aggarwal, Vishal Midha, Nicholas Sullivan (2023) Effect of Online Professional Network Recommendations on the Likelihood of an Interview: A Field Study. Information Systems Research 0(0). https://doi.org/10.1287/isre.2021.1053
  • Rohit Aggarwal, Vishal Midha, Nicholas Sullivan (2023) The Effect of Gender Expectations and Physical Attractiveness on Discussion of Weakness in Online Professional Recommendations. Information Systems Research 0(0). https://doi.org/10.1287/isre.2021.1032
"},{"location":"research/empirical/#content-consumption-sharing","title":"Content Consumption & Sharing","text":"
  • Hyelim Oh, Khim-Yong Goh, Tuan Q. Phan (2022) Are You What You Tweet? The Impact of Sentiment on Digital News Consumption and Social Media Sharing. Information Systems Research 0(0). source
  • Barnea, U., Meyer, R. J., & Nave, G. (2023). The Effects of Content Ephemerality on Information Processing. Journal of Marketing Research, 0(0). https://doi.org/10.1177/00222437221131047
"},{"location":"research/empirical/#news","title":"News","text":"
  • O\u2019Riordan, S., Emerson, B., Feller, J., & Kiely, G. (2023). The Road to Open News: A Theory of Social Signaling in an Open News Production Community. Journal of Management Information Systems, 40(1), 130-162. https://doi.org/10.1080/07421222.2023.2172777
"},{"location":"research/empirical/#e-commerce-online-shopping","title":"E-Commerce & Online Shopping","text":"
  • McKnight, D. Harrison, Vivek Choudhury, and Charles Kacmar. \"Developing and validating trust measures for e-commerce: An integrative typology.\" Information systems research 13.3 (2002): 334-359. source
  • Shang, Rong-An, Yu-Chen Chen, and Lysander Shen. \"Extrinsic versus intrinsic motivations for consumers to shop on-line.\" Information & management 42.3 (2005): 401-413. source
  • Kim, Hee-Woong, Hock Chuan Chan, and Atreyi Kankanhalli. \"What motivates people to purchase digital items on virtual community websites? The desire for online self-presentation.\" Information systems research 23.4 (2012): 1232-1245. source
  • Pavlou, Paul A. \"Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model.\" International journal of electronic commerce 7.3 (2003): 101-134. source
  • Arvind K. Tripathi, Young-Jin Lee, Amit Basu (2022) Analyzing the Impact of Public Buyer\u2013Seller Engagement During Online Auctions. Information Systems Research 0(0). source
  • Khan, A., & Krishnan, S. (2022). Ethical Behavior of Firms and B2C E-commerce Diffusion: Exploring the Mediating Roles of Customer Orientation and Innovation Capacity. Communications of the Association for Information Systems, 50, pp-pp. source
  • Iyengar R, Park Y-H, Yu Q. The Impact of Subscription Programs on Customer Purchases. Journal of Marketing Research. 2022. source
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"},{"location":"research/empirical/#gig-economy","title":"Gig Economy","text":"
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"},{"location":"research/empirical/#conflict-with-work","title":"Conflict with Work","text":"
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"},{"location":"research/empirical/#cyberloafing","title":"Cyberloafing","text":"
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"},{"location":"research/empirical/#service-operations","title":"Service Operations","text":"
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"},{"location":"research/empirical/#matching-markets","title":"Matching Markets","text":"
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"},{"location":"research/empirical/#addiction","title":"Addiction","text":"
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"},{"location":"research/empirical/#abuse","title":"Abuse","text":"
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"},{"location":"research/empirical/#sports","title":"Sports","text":"
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"},{"location":"research/empirical/#financial-technology-fintech","title":"Financial Technology (Fintech)","text":"
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"},{"location":"research/empirical/#decision-making","title":"Decision Making","text":"
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"},{"location":"research/empirical/#sharing-economy","title":"Sharing Economy","text":"
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"},{"location":"research/empirical/#is-use","title":"IS Use","text":"
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"},{"location":"research/empirical/#anti-corruption","title":"Anti-corruption","text":"
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"},{"location":"research/empirical/#loyalty-program","title":"Loyalty Program","text":"
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"},{"location":"research/empirical/#donation","title":"Donation","text":"
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"},{"location":"research/empirical/#immigration","title":"Immigration","text":"
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"},{"location":"research/empirical/#production-and-collaboration","title":"Production and Collaboration","text":"
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"},{"location":"research/empirical/#education","title":"Education","text":"
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"},{"location":"research/empirical/#it-systems","title":"IT Systems","text":"
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"},{"location":"research/empirical/#license","title":"License","text":"

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Last Update On 2023-04-15.

"},{"location":"research/technical/","title":"Technical Model / Design Science","text":""},{"location":"research/technical/#on-design-science","title":"On Design Science","text":"
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"},{"location":"research/technical/#artificial-intelligence","title":"Artificial Intelligence","text":"
  • Nguyen, Q. N., Sidorova, A., & Torres, R. (2022). Artificial Intelligence in Business: A Literature Review and Research Agenda. Communications of the Association for Information Systems, 50, pp-pp. source
"},{"location":"research/technical/#deep-learning","title":"Deep Learning","text":"
  • Luyang Chen, Markus Pelger, Jason Zhu (2023) Deep Learning in Asset Pricing. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4695
  • Samtani, S., Zhu, H., Padmanabhan, B., Chai, Y., & Chen, H. (2023). Deep learning for information systems research. Journal of Management Information Systems. https://doi.org/10.1080/07421222.2023.2172772
"},{"location":"research/technical/#reinforcement-learning","title":"Reinforcement Learning","text":"
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  • Arulkumaran, Kai, et al. \"Deep reinforcement learning: A brief survey.\" IEEE Signal Processing Magazine 34.6 (2017): 26-38. source
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  • Li, Yuxi. \"Reinforcement learning applications.\" arXiv preprint arXiv:1908.06973 (2019). source
  • Liebman, Elad, Maytal Saar-Tsechansky, and Peter Stone. \"The Right Music at the Right Time: Adaptive Personalized Playlists Based on Sequence Modeling.\" MIS Quarterly 43.3 (2019). source
  • Wang, Hao-nan, et al. \"Deep reinforcement learning: a survey.\" Frontiers of Information Technology & Electronic Engineering (2020): 1-19. source
  • Parker-Holder, Jack, et al. \"Automated Reinforcement Learning (AutoRL): A Survey and Open Problems.\" arXiv preprint arXiv:2201.03916 (2022). source
  • Mark Sellke, Aleksandrs Slikvins (2022) The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity. Operations Research 0(0). source
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  • Yi Zhu, Jing Dong, Henry Lam (2023) Uncertainty Quantification and Exploration for Reinforcement Learning. Operations Research 0(0). https://doi.org/10.1287/opre.2023.2436
"},{"location":"research/technical/#self-supervised-learning","title":"Self-Supervised Learning","text":"
  • Jing, Longlong, and Yingli Tian. \"Self-supervised visual feature learning with deep neural networks: A survey.\" IEEE transactions on pattern analysis and machine intelligence 43.11 (2020): 4037-4058. source
  • Xie, Yaochen, et al. \"Self-supervised learning of graph neural networks: A unified review.\" arXiv preprint arXiv:2102.10757 (2021). source
  • Liu, Yixin, et al. \"Graph self-supervised learning: A survey.\" arXiv preprint arXiv:2103.00111 (2021). source
  • Jaiswal, Ashish, et al. \"A survey on contrastive self-supervised learning.\" Technologies 9.1 (2021): 2. source
  • Liu, Xiao, et al. \"Self-supervised learning: Generative or contrastive.\" IEEE Transactions on Knowledge and Data Engineering (2021). source
"},{"location":"research/technical/#transfer-learning","title":"Transfer Learning","text":"
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  • Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. \"A survey of transfer learning.\" Journal of Big data 3.1 (2016): 1-40. source
  • Tan, Chuanqi, et al. \"A survey on deep transfer learning.\" International conference on artificial neural networks. Springer, Cham, 2018. source
  • Zhuang, Fuzhen, et al. \"A comprehensive survey on transfer learning.\" Proceedings of the IEEE 109.1 (2020): 43-76. source
"},{"location":"research/technical/#differential-privacy","title":"Differential Privacy","text":"
  • Dwork, Cynthia, et al. \"Calibrating noise to sensitivity in private data analysis.\" Theory of cryptography conference. Springer, Berlin, Heidelberg, 2006. source
  • Zheng, Qinqing, et al. \"Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion.\" arXiv preprint arXiv:2003.04493 (2020). source
  • Goodfellow, Ian. \"Efficient per-example gradient computations.\" arXiv preprint arXiv:1510.01799 (2015). source
  • Abadi, Martin, et al. \"Deep learning with differential privacy.\" Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. 2016. source
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  • McMahan, H. Brendan, et al. \"A general approach to adding differential privacy to iterative training procedures.\" arXiv preprint arXiv:1812.06210 (2018). source
  • Mironov, Ilya, Kunal Talwar, and Li Zhang. \"R\u00e9nyi Differential Privacy of the Sampled Gaussian Mechanism.\" arXiv preprint arXiv:1908.10530 (2019). source
  • Dwork, Cynthia, and Aaron Roth. \"The algorithmic foundations of differential privacy.\" Foundations and Trends in Theoretical Computer Science 9.3-4 (2014): 211-407. source
  • Dwork, Cynthia, and Adam Smith. \"Differential privacy for statistics: What we know and what we want to learn.\" Journal of Privacy and Confidentiality 1.2 (2010). source
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  • Jiang, Honglu, et al. \"Differential Privacy and Its Applications in Social Network Analysis: A Survey.\" arXiv preprint arXiv:2010.02973 (2020). source
  • Yang, Mengmeng, et al. \"Local differential privacy and its applications: A comprehensive survey.\" arXiv preprint arXiv:2008.03686 (2020). source
"},{"location":"research/technical/#explainable-ml-dl-ai","title":"Explainable ML / DL / AI","text":"
  • Angelino, Elaine, et al. \"Learning certifiably optimal rule lists for categorical data.\" arXiv preprint arXiv:1704.01701 (2017). source
  • Lundberg, Scott M., and Su-In Lee. \"A unified approach to interpreting model predictions.\" Advances in neural information processing systems 30 (2017). source
  • Lipton, Zachary C. \"The mythos of model interpretability.\" Queue 16.3 (2018): 31-57. source
  • Lundberg, Scott M., et al. \"From local explanations to global understanding with explainable AI for trees.\" Nature machine intelligence 2.1 (2020): 56-67. source
  • Molnar, Christoph. Interpretable machine learning. 2020. source
  • Arrieta, Alejandro Barredo, et al. \"Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI.\" Information Fusion 58 (2020): 82-115. source
  • Wang, Zhuo, et al. \"Scalable Rule-Based Representation Learning for Interpretable Classification.\" arXiv preprint arXiv:2109.15103 (2021). source
  • Chen, Valerie, et al. \"Interpretable machine learning: Moving from mythos to diagnostics.\" Communications of the ACM, August 2022, Vol. 65 No. 8, Pages 43-50. source
"},{"location":"research/technical/#fairness","title":"Fairness","text":"
  • Aum\u00fcller, Martin, Rasmus Pagh, and Francesco Silvestri. \"Fair near neighbor search: Independent range sampling in high dimensions.\" Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems. 2020. source
  • Krakovsky, Marina. \"## Formalizing Fairness.\" Communications of the ACM, August 2022, Vol. 65 No. 8, Pages 11-13. source
  • Dong, Yushun, et al. \"Fairness in Graph Mining: A Survey.\" arXiv preprint arXiv:2204.09888 (2022). source
"},{"location":"research/technical/#active-learning","title":"Active Learning","text":"
  • Aggarwal, C. C., Kong, X., Gu, Q., Han, J., & Yu, P. S. (2014). \"Active learning: A survey\". In Data Classification: Algorithms and Applications (pp. 571-605). CRC Press. source
  • Ren, Pengzhen, et al. \"A Survey of Deep Active Learning.\" ArXiv:2009.00236 [Cs, Stat], Aug. 2020. arXiv.org. source
  • Atahan, Pelin, and Sumit Sarkar. \"Accelerated learning of user profiles.\" Management Science 57.2 (2011): 215-239. source
"},{"location":"research/technical/#label-imbalance","title":"Label Imbalance","text":"
  • Nasir, Murtaza, et al. \"Improving Imbalanced Machine Learning with Neighborhood-Informed Synthetic Sample Placement.\" Journal of Management Information Systems 39.4 (2022): 1116-1145. https://doi.org/10.1080/07421222.2022.2127453
"},{"location":"research/technical/#label-noise","title":"Label Noise","text":"
  • Han, Bo, et al. \"A survey of label-noise representation learning: Past, present and future.\" arXiv preprint arXiv:2011.04406 (2020). source
"},{"location":"research/technical/#natural-language-processing","title":"Natural Language Processing","text":""},{"location":"research/technical/#text-summarization","title":"Text Summarization","text":"
  • Rush, Alexander M., Sumit Chopra, and Jason Weston. \"A neural attention model for abstractive sentence summarization.\" arXiv preprint arXiv:1509.00685 (2015). source
  • Chen, Yen-Chun, and Mohit Bansal. \"Fast abstractive summarization with reinforce-selected sentence rewriting.\" arXiv preprint arXiv:1805.11080 (2018). source
  • Gehrmann, Sebastian, Yuntian Deng, and Alexander M. Rush. \"Bottom-up abstractive summarization.\" arXiv preprint arXiv:1808.10792 (2018). source
"},{"location":"research/technical/#topic-modeling","title":"Topic Modeling","text":"
  • Jelodar, Hamed, et al. \"Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey.\" Multimedia Tools and Applications 78.11 (2019): 15169-15211. source
  • Qiang, Jipeng, et al. \"Short text topic modeling techniques, applications, and performance: a survey.\" IEEE Transactions on Knowledge and Data Engineering (2020). source
  • Vayansky, Ike, and Sathish AP Kumar. \"A review of topic modeling methods.\" Information Systems 94 (2020): 101582. source
  • Kherwa, Pooja, and Poonam Bansal. \"Topic modeling: a comprehensive review.\" EAI Endorsed transactions on scalable information systems 7.24 (2020). source
  • Chauhan, Uttam, and Apurva Shah. \"Topic Modeling Using Latent Dirichlet allocation: A Survey.\" ACM Computing Surveys (CSUR) 54.7 (2021): 1-35. source
  • Yi Yang, Kunpeng Zhang, Yangyang Fan (2022) sDTM: A Supervised Bayesian Deep Topic Model for Text Analytics. Information Systems Research 0(0). source
  • Li, Weifeng and Chen, Hsinchun. 2022. \"Discovering Emerging Threats in the Hacker Community: A Nonparametric Emerging Topic Detection Framework,\" MIS Quarterly, (46: 4) pp.2337-2350. source
"},{"location":"research/technical/#personalized-feedback","title":"Personalized Feedback","text":"
  • Jiyeon Hong, Paul R. Hoban (2022) Writing More Compelling Creative Appeals: A Deep Learning-Based Approach. Marketing Science 0(0). source
"},{"location":"research/technical/#sentiment-analysis","title":"Sentiment Analysis","text":"
  • Rocklage, M. D., He, S., Rucker, D. D., & Nordgren, L. F. (2023). Beyond Sentiment: The Value and Measurement of Consumer Certainty in Language. Journal of Marketing Research, 0(0). https://doi.org/10.1177/00222437221134802
"},{"location":"research/technical/#decentralized-learning","title":"Decentralized Learning","text":"
  • Li, Tian, et al. \"Federated learning: Challenges, methods, and future directions.\" IEEE Signal Processing Magazine 37.3 (2020): 50-60. source
  • Lim, Wei Yang Bryan, et al. \"Federated learning in mobile edge networks: A comprehensive survey.\" IEEE Communications Surveys & Tutorials 22.3 (2020): 2031-2063. source
  • Mothukuri, Viraaji, et al. \"A survey on security and privacy of federated learning.\" Future Generation Computer Systems 115 (2021): 619-640. source
  • Kairouz, Peter, et al. \"Advances and open problems in federated learning.\" Foundations and Trends\u00ae in Machine Learning 14.1\u20132 (2021): 1-210. source
  • Warnat-Herresthal, Stefanie, et al. \"Swarm learning for decentralized and confidential clinical machine learning.\" Nature 594.7862 (2021): 265-270. source code
  • Kallista Bonawitz, et al. 2022. Federated learning and privacy. Commun. ACM 65, 4 (April 2022), 90\u201397. source
"},{"location":"research/technical/#personality-measurement","title":"Personality Measurement","text":"
  • Kai Yang, Raymond Y. K. Lau, Ahmed Abbasi (2022) Getting Personal: A Deep Learning Artifact for Text-Based Measurement of Personality. Information Systems Research 0(0). source
"},{"location":"research/technical/#adversaries","title":"Adversaries","text":"
  • Li, Weifeng, and Yidong Chai. \"Assessing and Enhancing Adversarial Robustness of Predictive Analytics: An Empirically Tested Design Framework.\" Journal of Management Information Systems 39.2 (2022): 542-572. source
"},{"location":"research/technical/#data-imputation","title":"Data Imputation","text":"
  • Lin, Wei-Chao, and Chih-Fong Tsai. \"Missing value imputation: a review and analysis of the literature (2006\u20132017).\" Artificial Intelligence Review 53.2 (2020): 1487-1509. source
  • Hasan, Md Kamrul, et al. \"Missing value imputation affects the performance of machine learning: A review and analysis of the literature (2010\u20132021).\" Informatics in Medicine Unlocked 27 (2021): 100799. source
"},{"location":"research/technical/#application","title":"Application","text":"
  • Aiken, Emily, et al. \"Machine learning and phone data can improve targeting of humanitarian aid.\" Nature (2022): 1-7. source
  • Nan Zhang, Heng Xu (2023) Fairness of Ratemaking for Catastrophe Insurance: Lessons from Machine Learning. Information Systems Research 0(0). https://doi.org/10.1287/isre.2022.1195
  • Arindam Ray, Wolfgang Jank, Kaushik Dutta, Matthew Mullarkey (2023) An LSTM+ Model for Managing Epidemics: Using Population Mobility and Vulnerability for Forecasting COVID-19 Hospital Admissions. INFORMS Journal on Computing 0(0). https://doi.org/10.1287/ijoc.2023.1269
"},{"location":"research/technical/#conversational-agents","title":"Conversational Agents","text":"
  • Elshan, E., Ebel, P., S\u00f6llner, M., & Leimeister, J. M. (2023). Leveraging Low Code Development of Smart Personal Assistants: An Integrated Design Approach with the SPADE Method. Journal of Management Information Systems, 40(1), 96-129. https://doi.org/10.1080/07421222.2023.2172776
"},{"location":"research/technical/#transparency","title":"Transparency","text":"
  • Bitzer, T., Wiener, M., & Cram, W. (2023). Algorithmic Transparency: Concepts, Antecedents, and Consequences \u2013 A Review and Research Framework. Communications of the Association for Information Systems, 52, pp-pp. https://aisel.aisnet.org/cais/vol52/iss1/16
"},{"location":"research/technical/#graph-and-network","title":"Graph And Network","text":""},{"location":"research/technical/#graph-neural-network","title":"Graph Neural Network","text":"
  • Kipf, T. N. \"Deep learning with graph-structured representations.\" (2020). pdf
  • Wu, Zonghan, et al. \"A comprehensive survey on graph neural networks.\" IEEE Transactions on Neural Networks and Learning Systems (2020). source
  • Zhou, Jie, et al. \"Graph neural networks: A review of methods and applications.\" arXiv preprint arXiv:1812.08434 (2018). source
  • Zhang, Chuxu, et al. \"Heterogeneous graph neural network.\" Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019. source
  • Wang, Xiao, et al. \"Heterogeneous graph attention network.\" The World Wide Web Conference. 2019. source
  • Hu, Ziniu, et al. \"Heterogeneous graph transformer.\" Proceedings of The Web Conference 2020. 2020. source
"},{"location":"research/technical/#graph-embedding","title":"Graph Embedding","text":"
  • Goyal, Palash, and Emilio Ferrara. \"Graph embedding techniques, applications, and performance: A survey.\" Knowledge-Based Systems 151 (2018): 78-94. source
  • Xi Chen, Yan Liu, Cheng Zhang (2022) Distinguishing Homophily from Peer Influence Through Network Representation Learning. INFORMS Journal on Computing 0(0). source
"},{"location":"research/technical/#graphical-causality","title":"Graphical Causality","text":"
  • Bernhard Sch\u00f6lkopf, et al. \"Towards Causal Representation Learning.\" (2021). source
"},{"location":"research/technical/#influence-maximization","title":"Influence Maximization","text":"
  • Li, Yuchen, et al. \"Influence maximization on social graphs: A survey.\" IEEE Transactions on Knowledge and Data Engineering 30.10 (2018): 1852-1872. source
  • Banerjee, Suman, Mamata Jenamani, and Dilip Kumar Pratihar. \"A survey on influence maximization in a social network.\" Knowledge and Information Systems 62.9 (2020): 3417-3455. source
  • De Nittis, Giuseppe, and Nicola Gatti. \"How to maximize the spread of social influence: A survey.\" arXiv preprint arXiv:1806.07757 (2018). source
  • Ozan Candogan (2022) Persuasion in Networks: Public Signals and Cores. Operations Research 0(0). source
"},{"location":"research/technical/#vertical-markets","title":"Vertical Markets","text":"
  • Soheil Ghili (2022) Network Formation and Bargaining in Vertical Markets: The Case of Narrow Networks in Health Insurance. Marketing Science 0(0). source
"},{"location":"research/technical/#network-structures","title":"Network Structures","text":"
  • Sinan Aral, Paramveer S. Dhillon (2022) What (Exactly) Is Novelty in Networks? Unpacking the Vision Advantages of Brokers, Bridges, and Weak Ties. Management Science 0(0). source
  • Schecter, Aaron, Omid Nohadani, and Noshir Contractor. \"A Robust Inference Method for Decision Making in Networks.\" Management Information Systems Quarterly 46.2 (2022): 713-738. source
  • Syngjoo Choi, Sanjeev Goyal, Frederic Moisan, Yu Yang Tony To (2023) Learning in Networks: An Experiment on Large Networks with Real-World Features. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4680
"},{"location":"research/technical/#network-privacy","title":"Network Privacy","text":"
  • Marcella Hastings, Brett Hemenway Falk, Gerry Tsoukalas (2022) Privacy-Preserving Network Analytics. Management Science 0(0). source
"},{"location":"research/technical/#recommendation-systems","title":"Recommendation Systems","text":""},{"location":"research/technical/#recommendation-objectives","title":"Recommendation Objectives","text":"
  • Gunawardana, Asela, and Guy Shani. \"A survey of accuracy evaluation metrics of recommendation tasks.\" Journal of Machine Learning Research 10.12 (2009). source
  • Kunaver, Matev\u017e, and Toma\u017e Po\u017erl. \"Diversity in recommender systems\u2013A survey.\" Knowledge-based systems 123 (2017): 154-162. source
  • Kaminskas, Marius, and Derek Bridge. \"Diversity, serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems.\" ACM Transactions on Interactive Intelligent Systems (TiiS) 7.1 (2016): 1-42. source
  • Wu, Qiong, et al. \"Recent advances in diversified recommendation.\" arXiv preprint arXiv:1905.06589 (2019). source
  • Wu, Le, et al. \"A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation.\" IEEE Transactions on Knowledge and Data Engineering (2022). source
  • Alhijawi, Bushra, Arafat Awajan, and Salam Fraihat. \"Survey on the Objectives of Recommender System: Measures, Solutions, Evaluation Methodology, and New Perspectives.\" ACM Computing Surveys (CSUR) (2022). source
"},{"location":"research/technical/#dataset","title":"Dataset","text":"
  • Gao, Chongming, et al. \"KuaiRec: A Fully-observed Dataset for Recommender Systems.\" arXiv preprint arXiv:2202.10842 (2022). source web
  • Chin, Jin Yao, Yile Chen, and Gao Cong. \"The Datasets Dilemma: How Much Do We Really Know About Recommendation Datasets?.\" Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. 2022. source
"},{"location":"research/technical/#link-prediction","title":"Link Prediction","text":"
  • L\u00fc, Linyuan, and Tao Zhou. \"Link prediction in complex networks: A survey.\" Physica A: statistical mechanics and its applications 390.6 (2011): 1150-1170. source
  • L\u00fc, Linyuan, and Tao Zhou. \"Link prediction in complex networks: A survey.\" Physica A: statistical mechanics and its applications 390.6 (2011): 1150-1170. https://doi.org/10.1145/3012704
  • Wang, P., Xu, B., Wu, Y. et al. Link prediction in social networks: the state-of-the-art. Sci. China Inf. Sci. 58, 1\u201338 (2015). https://doi.org/10.1007/s11432-014-5237-y
  • Kim, J., Diesner, J. Formational bounds of link prediction in collaboration networks. Scientometrics 119, 687\u2013706 (2019). https://doi.org/10.1007/s11192-019-03055-6
  • Kumar, Ajay, et al. \"Link prediction techniques, applications, and performance: A survey.\" Physica A: Statistical Mechanics and its Applications 553 (2020): 124289. source
  • Qin, Meng, and Dit-Yan Yeung. \"Temporal Link Prediction: A Unified Framework, Taxonomy, and Review.\" arXiv preprint arXiv:2210.08765 (2022). https://doi.org/10.48550/arXiv.2210.08765
  • Wu, H., Song, C., Ge, Y. et al. Link Prediction on Complex Networks: An Experimental Survey. Data Sci. Eng. 7, 253\u2013278 (2022). https://doi.org/10.1007/s41019-022-00188-2
"},{"location":"research/technical/#recommendation-framework","title":"Recommendation Framework","text":"
  • Anelli, Vito Walter, et al. \"Elliot: a comprehensive and rigorous framework for reproducible recommender systems evaluation.\" Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2021. source code
  • TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale recommender systems (RecSys). It allows authors to train models with large embedding tables sharded across many GPUs.
  • Spotlight uses PyTorch to build both deep and shallow recommender models. By providing both a slew of building blocks for loss functions (various pointwise and pairwise ranking losses), representations (shallow factorization representations, deep sequence models), and utilities for fetching (or generating) recommendation datasets, it aims to be a tool for rapid exploration and prototyping of new recommender models.
  • MTReclib provides a PyTorch implementation of multi-task recommendation models and common datasets.
  • The repository microsoft/recommenders contains examples and best practices for building recommendation systems, provided as Jupyter notebooks.
  • Surprise is a Python scikit for building and analyzing recommender systems that deal with explicit rating data.
  • The repository hiroyuki-kasai/NMFLibrary is a pure-Matlab library of a collection of algorithms of non-negative matrix factorization (NMF).
  • QMF is a fast and scalable C++ library for implicit-feedback matrix factorization models (WALS and BPR).
  • The repository benfred/implicit provides fast Python implementations of several different popular recommendation algorithms for implicit feedback datasets.
  • This repository liu-yihong/BPRH implements the Bayesian personalized ranking method for heterogeneous implicit feedback.
  • reXmeX is recommender system evaluation metric library. It consists of utilities for recommender system evaluation. First, it provides a comprehensive collection of metrics for the evaluation of recommender systems. Second, it includes a variety of methods for reporting and plotting the performance results. Implemented metrics cover a range of well-known metrics and newly proposed metrics from data mining conferences and prominent journals.
"},{"location":"research/technical/#sequential-recommendation-systems","title":"Sequential Recommendation Systems","text":"
  • Quadrana, Massimo, Paolo Cremonesi, and Dietmar Jannach. \"Sequence-aware recommender systems.\" ACM Computing Surveys (CSUR) 51.4 (2018): 1-36. source
  • Maher, Mohamed, et al. \"Comprehensive Empirical Evaluation of Deep Learning Approaches for Session-based Recommendation in E-Commerce.\" arXiv preprint arXiv:2010.12540 (2020). source
  • Fang, Hui, et al. \"Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations.\" ACM Transactions on Information Systems (TOIS) 39.1 (2020): 1-42. source
  • Latifi, Sara, Noemi Mauro, and Dietmar Jannach. \"Session-aware recommendation: A surprising quest for the state-of-the-art.\" Information Sciences 573 (2021): 291-315. source
  • Wang, Shoujin, et al. \"A survey on session-based recommender systems.\" ACM Computing Surveys (CSUR) 54.7 (2021): 1-38. source
  • Wen Wang, Beibei Li, Xueming Luo, Xiaoyi Wang (2022) Deep Reinforcement Learning for Sequential Targeting. Management Science 0(0). source
  • Omid Rafieian (2022) Optimizing User Engagement Through Adaptive Ad Sequencing. Marketing Science 0(0). source
  • Yifu Li, Christopher Thomas Ryan, Lifei Sheng (2023) Optimal Sequencing in Single-Player Games. Management Science 0(0). https://doi.org/10.1287/mnsc.2022.4665
  • Marios Kokkodis, Panagiotis G. Ipeirotis (2023) The Good, the Bad, and the Unhirable: Recommending Job Applicants in Online Labor Markets. Management Science 0(0). https://doi.org/10.1287/mnsc.2023.4690
"},{"location":"research/technical/#user-item-matrix-factorization","title":"User-Item Matrix Factorization","text":"
  • Su, Xiaoyuan, and Taghi M. Khoshgoftaar. \"A survey of collaborative filtering techniques.\" Advances in artificial intelligence 2009 (2009). source
  • Cacheda, Fidel, et al. \"Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems.\" ACM Transactions on the Web (TWEB) 5.1 (2011): 1-33. source
  • Shi, Yue, Martha Larson, and Alan Hanjalic. \"Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges.\" ACM Computing Surveys (CSUR) 47.1 (2014): 1-45. source
  • Han, Soyeon Caren, et al. \"GLocal-K: Global and Local Kernels for Recommender Systems.\" Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2021. source
  • Rendle, Steffen, et al. \"Neural collaborative filtering vs. matrix factorization revisited.\" Fourteenth ACM conference on recommender systems. 2020. source
"},{"location":"research/technical/#graph-neural-network-based-recommendation","title":"Graph Neural Network Based Recommendation","text":"
  • Wu, Shiwen, et al. \"Graph neural networks in recommender systems: a survey.\" arXiv preprint arXiv:2011.02260 (2020). source
"},{"location":"research/technical/#reinforcement-learning-based-recommendation","title":"Reinforcement Learning Based Recommendation","text":"
  • Lin, Yuanguo, et al. \"A Survey on Reinforcement Learning for Recommender Systems.\" arXiv preprint arXiv:2109.10665 (2021). source
"},{"location":"research/technical/#causal-learning","title":"Causal Learning","text":"
  • Si, Zihua et al. \u201cA Model-Agnostic Causal Learning Framework for Recommendation using Search Data.\u201d (2022). source code
"},{"location":"research/technical/#self-supervised-learning_1","title":"Self-Supervised Learning","text":"
  • Yu, Junliang, et al. \"Self-Supervised Learning for Recommender Systems: A Survey.\" arXiv preprint arXiv:2203.15876 (2022). source
"},{"location":"research/technical/#debias","title":"Debias","text":"
  • Schnabel, Tobias, et al. \"Recommendations as treatments: Debiasing learning and evaluation.\" international conference on machine learning. PMLR, 2016. source
  • Chen, Jiawei, et al. \"AutoDebias: Learning to Debias for Recommendation.\" arXiv preprint arXiv:2105.04170 (2021). source
  • Jiawei Chen on github.com provides a repository at jiawei-chen/RecDebiasing
"},{"location":"research/technical/#user-reviews-for-recommendation","title":"User Reviews for Recommendation","text":"
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"},{"location":"research/technical/#e-commerce","title":"E-Commerce","text":"
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"},{"location":"research/technical/#license","title":"License","text":"

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